The international journal of advanced manufacturing technology, tập 58, số 1 4, 2012

ORIGINAL ARTICLEgraded nano-composite ceramic cutting tools Guangming Zheng&Jun Zhao&Zhongjun Gao& Qingyuan Cao Received: 12 February 2011 / Accepted: 3 May 2011 / Published online: 15 M

ORIGINAL ARTICLE

Comparison of cranioplasty implants produced

by machining and by casting in a gypsum mold

Dalberto Dias da Costa&Sérgio Fernando Lajarin

Received: 12 August 2009 / Accepted: 12 May 2011 / Published online: 24 May 2011

# Springer-Verlag London Limited 2011

Abstract Cranioplasty is a medical technique used to

correct craniofacial defects Depending on the size and

location of the defect, a bone substitute to replace the

deformed or missing tissue can be manufactured With the

advances in computer-based systems and the invention of

new biomaterials, the production of customized implants

with good cosmetic and functional results has now become

widespread However, little research has been undertaken

into the quality of prefabricated specimens in terms of

dimensional and form errors Because of the geometric

complexity involved, measurement of this kind of object is

a complicated process The aim of this paper is to describe

two different manufacturing processes used to produce a

large polymethylmethacrylate (PMMA) implant for use in

cranioplastic surgery and to discuss the results of the

evaluation of the dimensional errors and lead times

associated with these methods In the first method, the

specimen was directly machined from an acrylic block In

the second, the implant was cast in a machined gypsum

mold Both processes were based on a digital model of a

dried human skull scanned by computer tomography

Dimensional errors were evaluated with a coordinated

measurement machine Despite their complexity, the

PMMA specimens produced were measured and their

dimensional differences established Compared with direct

machining, casting results in a longer lead time and,because of shrinkage, a larger dimensional deviation.Keywords Cranioplasty Cast implants Direct machining

1 IntroductionThe use of prefabricated alloplastic implants for cranio-plasty applications has grown in recent years, mainlybecause such implants help reduce surgical time and allow

a satisfactory esthetic restoration, as has been described byseveral researchers [1–5]

Firstly, the injured region, or in some cases the wholeskull, is scanned by computer tomography (CT) Theacquired image set is then processed to separate the region

of interest and the edges making up the bone contours Anumber of commercial packages are currently available forthis kind of application, and some are able to produce a 3Dreconstruction of the scanned volume and export it in InitialGraphics Exchange Specification or Standard TessellationLanguage (STL) format

Once a computer-aided design (CAD) model has beenproduced, it can be adjusted digitally to facilitate attach-ment of the prosthesis as proposed by Weihe et al [6].Depending on the geometric complexity and biomaterialchosen, one or more manufacturing processes can beselected In most cases, more than one manufacturingprocess is usually required to produce the implant

A number of different manufacturing alternatives havebeen studied and recommended for the prefabrication ofcranioplasty implants Casting, machining, forming, andlayer manufacturing-based processes are the most signifi-cant examples As described by Giannatsis and Dedoussis[7], Leong et al [8] and Yang et al [9], the last of these

D D da Costa (*)

Mechanical Engineering Department,

Universidade Federal do Paraná,

Curitiba, PR, Brazil

e-mail: dalberto@ufpr.br

S F Lajarin

Postgraduate Program in Mechanical Engineering,

Universidade Federal do Paraná,

Paraná,

Curitiba, PR, Brazil

DOI 10.1007/s00170-011-3388-1

techniques allows very intricate geometric forms to be

produced and is used to produce biomodels and scaffolds,

which have been the object of much attention from the

research community in recent years

Direct machining is a very flexible process and has been

used in the production of titanium [6] and acrylic implants

[10] Its most important limitation is the interaction

(gouging) between the cutting tool and the blind, or even

small, cavities found in the surface of the implant

However, as described next, the adoption of smaller cutting

tools in the finishing phase can minimize this kind of

geometric constraint, particularly when such cavities do not

represent important anatomical features In addition to the

problems, they pose in terms of gouging, the free-form

surfaces found in implants pose serious difficulties for setup

planning insofar as determining datum and fixtures is

concerned Nevertheless, if satisfactory fixture planning

can be developed, machining can be considered an

alternative

The use of casts offers one significant advantage over

other techniques, namely, the possibility of producing

composite materials in the same mold, as pointed out by

Schiller et al [11] The cost of making the mold, however,

is one of the shortcomings of this approach A combination

of machining and casting, as proposed by Hieu et al [12]

and Weihe et al [6] represents a valuable alternative, since

a cheaper free-machining material could be used to build

the mold cavities

As well as increasing the lead time, a combination of

different manufacturing techniques in the production chain

of a cranioplasty implant affects the final quality of the

implant, in particular its shape and dimensional deviation

A further, significant problem associated with dimensional

error assessment in implants arises as a result of their

geometric complexity, which makes traditional linear

measurements more difficult to apply [13]

The aim of this paper is to describe two different

manufacturing processes that were used to produce a large

polymethylmethacrylate (PMMA) implant for use in

cranio-plastic surgery and to discuss the results of the evaluation

of the dimensional errors and lead times associated with

these methods The longer manufacturing chain involves

mold machining and casting; and the shorter chain, direct

machining of the region modeled

2 Materials and methods

The starting point for this work was a dried human skull

that had been tomographed and modeled by Bazan [10] and

had its calvarial region digitally extracted as shown in

Fig 1 Despite the fact that it is unique and does not

correspond to a real cranial defect, the digitally extracted

region represents a substantial challenge both to measureand manufacture, as it contains two highly curved surfaces(the internal one and the external one) The third surfacewas defined by an arbitrary intersection of the digital model

of the skull with a plane parallel to the scanning plane,which is roughly parallel to the occlusal plane The details

of the CT and CAD modeling can be seen in reference [10]and are not repeated here

PMMA was chosen in this study because it is extensivelyused as biomaterial, is cheap, and can be easily manufac-tured [3, 12, 14] The choice of autopolymerizing ratherthan heat-polymerizing material was influenced by thedesign of the mold and is explained in the next section.2.1 Direct machining

A prepolymerized powder was hand mixed with the liquidmonomer (Classico São Paulo, Brazil) inside an open box.Based on the results reported by Jasper et al [15], theliquid-to-powder ratio adopted was 0.5 mL/g The box wasthen kept inside an autoclave with a positive pressure of

300 kPa, and the rectangular block formed (57×146×

175 mm) was removed from the box 2 h later

Most of the machining conditions were very similar tothose used by Bazan [10], the main difference being the use

of smaller cutting tools and the addition of a parallel lacemilling strategy during the finishing of both the concaveFig 1 Dried skull and the digital model of the region extracted

and convex surfaces The setup was the same as that used

by Bazan [10] and involved the use of a sacrificial material

for the second fixturing and localization Schematic

representations of the machining strategies for both the

surfaces are shown in Figs.2 and 3, and the main cutting

conditions are given in Table 1 All the machining was

planned with Edgecam software (Pathtrace Ltd., Reading,

UK) and executed in a Discovery 4022 three-axis vertical

machining center (Romi, São Paulo, Brazil)

2.2 Casting

In most of the literature about acrylic castings for

cranioplasty applications, the molds are produced by hand

after the cranial defect has been copied using alginate or

similar material [16] or by layer manufacture of an implant

model [7] In both cases, the models are then used to create

a gypsum-filled mold

The use of mold machining is rare Hieu et al [12]

proposed this technique as a way of achieving greater

quality and reducing cost compared with layer

manufacturing-based processes All the parts of the molds

(cores and cavities) they used in their study were machined

in hardwood resins and plastics

In this work, we propose a different approach (see

Fig 4) involving the design of a mold based on an

aluminum flask that can be reused and filled up with

gypsum as necessary The top of the flask can be moved

along a two pin guide so that external pressure can be

applied with a press

The first gypsum block was cast in the flask and on the

top plate After it had set, it was removed from the flask

while being kept anchored to the top plate by means of

machined grooves as shown in Fig.4 After the flask had

been emptied, the second block was cast and kept there

until the end of the whole process The gypsum casts were

made from type IV dental stone, and the water-to-powder

ratio was 0.2 mL/g in accordance with the manufacturer’s

instructions (Zhermack SpA, Badia Polesine, Italy)

The machining planning and conditions were the same

as those used for the PMMA milling, which are shown in

Figs 2 and 3 and Table 1 A circular groove was milledalong the upper surface of the gypsum and a rubber O-ringwas inserted in the groove to provide a mechanical sealduring the pressing phase

A sufficient volume of PMMA mixture was prepared toprovide the 150 mL required for the casting itself and theexcess portion that flows out of the mold, thus guaranteeingthat the mold would be completely filled Five minutes afterthe PMMA was prepared, the mold was closed and secured

to the table of a hydraulic press An axial load of 2 kN wasapplied for 2 h to guarantee complete polymerization.The setup and run times for each task were recorded toenable the process times for both manufacturing processes

to be compared

2.3 Dimensional error assessmentAfter the manufacturing phases had been completed, theskull, gypsum mold (core and die) and directly machinedand molded acrylic implants were all measured against thedigital STL model

Because the skull was a complete piece, its inner surfacewas not inspected All measurements were carried out with

a coordinated measuring machine (CMM) (Discovery IIfrom Sheffield, WI, USA) equipped with a 2-mm sphericaltouch tip and an accuracy of 5+L/200 μm PC-DMIS™

CAD++ software (Wilcox, UT, USA) was used for thelocalization procedure and measurement analysis Thispackage has a special “best-fit” resource based on theleast-squares method that allows the automatic localization

of complex parts The Design Coordinate System (DCS)was based on the STL model and used to determine theMeasurement Coordinate System (MCS) for all theinspected parts

The procedure to localize the MCS consisted of threesteps, which were applied to the seven surfaces Firstly,based on the “3-2-1 principle”, six points were manuallydefined to achieve rough localization In the second stage, agrid composed of 160 points was created in the DCS andused by the PC-DMIS localization algorithm This secondstage was applied iteratively until the system reportedFig 2 Milling operation at the concave surface

Fig 3 Milling operation at the convex surface

convergence In the last stage, a regular grid with 5,000

points was defined to cover the visible surfaces For every

digitized point, the difference (T) between the point in the

MCS and the corresponding point on the digital surface in

the DCS was computed according to the following

equation:

T ¼ i xmð xdÞ þj yð mydÞ þk zmð zdÞ ð1Þ

where the unit vector“i, j, k” defines the direction in which

the touch trigger approaches the surface,

xm, ym, and zmare the Cartesian coordinates of the point

measured in the MCS, and

Xd, yd, and zdare the Cartesian coordinates of the digital

model in the DCS

The root mean square (RMS) of the measurements was

used to estimate the dimensional deviation for the inspected

surfaces

In addition, to improve the RMS-based analysis, the

bounding boxes were calculated for the manufactured

specimens using the digitized points Their dimensions

were defined by the differences between the largest and

smallest values in the X, Y, and Z directions

3 ResultsThe machined surfaces resemble the surfaces in the digitalmodel very closely As shown in Fig 5, even smallanatomical marks, such as those in the calcified sutures,were reproduced

As aluminum alloys have good mechanical strength, thereusable mold case could be easily referenced and fixed tothe machine table The case also increased the stiffness ofthe gypsum, thus helping reproduce the small details found

in the STL model The machined core and cavity can beseen in Fig.6

As the core moves into the cavity, the excess PMMAflows out of the mold; once the core is fully inserted intothe cavity, the rubber O-ring forms a seal between the twoparts of the mold allowing a positive pressure to bemaintained during polymerization After the setting time,the casting was easily removed without damaging themachined gypsum As shown in Fig 7, minor flashformation alternating with small unfilled regions occurred

at the mold parting line The flashes were manually cut offbefore any measurements were taken

Table 1 Machining conditions

Cutting conditions Surface Operation Strategy Cutting speed (m/min) Feed per tooth (mm/rev) Cutting tool Concave End milling the sacrificial material Z constant 157 0.3 20 mm end mill

Second finishing pass at the end Parallel lace 44 0.22 4 mm ball nose

End milling to cut off the sacrificial material Z constant 44 0.22 4 mm end mill

Fig 4 Mold design

Figure 8 shows a histogram of the T values and a

graphical representation of the distribution of these values

over the skull surface generated with the PC-DMIS

software The darker areas (red and dark blue) indicate the

values outside a range of ±0.3 mm The maximum values

for T+ and T− are also identified A similar analysis was

conducted for all the surfaces inspected The results are

summarized in Table 2, which gives the RMS values and

the amplitude of the points measured

Table 3 gives the results of the bounding box

calcu-lations for the external surfaces of both the cast and the

machined specimen The values for the STL model were

used for comparative purposes

The elapsed times for each task in both processes are

shown in Tables4and5 The setup time includes planning,

machine preparation, and material handling The process

times are the sum of the setup or run times for each task in

the sequence in which they are carried out to produce a

single PMMA specimen

4 Discussion and conclusion

Starting from a digital STL model produced by Bazan [10] of

a large hypothetical cranioplasty implant, two specimens

were produced using two different manufacturing processes.The specimens, which were made of PMMA, wereevaluated with a CMM Dimensional error assessment wasbased on a comparison of the surfaces of the specimenswith the surfaces of the STL model Because of the highdegree of geometric complexity imposed by this kind ofsurface, PC-DMIS software was used to run an automaticlocalization procedure

As a real clinical case was not available for study, a largeregion of the skull corresponding to the top of thecalvarium was analyzed It is reasonable to suppose thatsuch a specimen is representative, from the point of view ofthe geometry, of a great number of the skull defectsreported in the specialized literature [17–19] Of course,

in the case of smaller implants, especially those with smallcavities, more effort is needed to design and machine thegypsum molds However, as pointed out by Hue et al [12],correct planning of the parting line and selection of smallcutting tools can minimize the problem, allowing the molds

to be satisfactorily milled in a three-axis machine tool.The largest RMS value (0.169 mm), which coincidedwith the second largest amplitude (T+=0.816 mm and

T−=−0.669 mm), was observed when the digital model(STL) was compared with the original dried skull Thisdifference can be attributed to three sources of error The

Fig 5 Comparison of the

digital STL model and

the machined specimen

Fig 6 Visual comparison of the

digital model (convex surface)

with the machined (concave)

gypsum cavity

first, which is known as the partial-volume effect, is a

consequence of the use of computed tomography As

pointed out by Mazzoli et al [13] and Bouyssié et al

[20], this kind of deviation depends on the scanning

parameters adopted, such as section thickness, pitch, tube

current, and voltage The second source is related to the 3D

reconstruction and factors such as the bone segmentation,

contour vectorization, tessellation, and interpolation

meth-ods The millimeter-to-pixel ratio adopted in the model

evaluated was 250/512, as reported by Bazan [10] Despite

the facilities available in the software for image

segmenta-tion, contour interpolation and tessellasegmenta-tion, a certain amount

of error, albeit small, can be expected from this kind of

processing Mazzoli et al [13] and Choi et al [21]

highlighted the importance of the threshold value adoptedduring image segmentation as a factor that has a significanteffect on the quality of the digital model The third source

of error can be attributed to the localization procedure.Despite the large point set adopted here, a certain amount oferror should be expected, which, as pointed out by Lai andChen [22], depends mainly on the quality of the pointsmeasured

The analysis in Fig 8helps to corroborate the last errorsource discussed above The histogram indicates a well-centered distribution of the T values, i.e., roughly 50% ofthe points are positive However, their spatial distributionover the skull surface reveals two patterns The first,corresponding to the dark blue area, is mainly composed

Fig 7 Cast implant with minor

flash formation at the mold

parting line

Fig 8 Histogram of T values

and a graphical representation

of these on the digital STL

model after measurements

of the skull were taken

of negative values less than−0.30 mm The second, which

follows the calcified sutures, contains positive values

greater than 0.30 mm (red area) and is a result of a

discontinuity in the modeled surface

The dimensional error found in the cast implant was less

for both surfaces (RMS=0.121 and 0.117 mm for the

external and internal surfaces, respectively) than that

observed for the skull, but with a larger amplitude (T+=

0.517 and T−=−1.373 mm for the internal surface) As with

the skull, a similar pattern for the more negative T values

was observed, but this is largely explained by the shrinkage

that occurs after the setting and curing time for the PMMA

This shrinkage can be confirmed by analysis of the bounding

box values given in Table3 The mean value of the difference

between the cast and the digital model was estimated to be

−0.63% However, this cannot be entirely attributed to

shrinkage alone as other sources of error are present, such

as the localization procedure, the machined gypsum mold,

and the distortion caused by demolding The value observed

lies in the linear shrinkage range reported by Keenan et al

[23] during injection molding of PMMA dentures

While according to Silikas et al [24], the estimated

theoretical value for the proportion of monomer used could

be expected to be larger, the smaller shrinkage observed in

the present study can be explained by the fact that positive

pressure was maintained throughout the polymerization

phase and, as reported by Gilbert el al [25], by the mixing

procedure adopted here, i.e., hand mixing instead ofvacuum mixing

As the PMMA specimen was cooled inside the mold, theexpansion and contraction caused by the exothermicreaction during polymerization were constrained by themold walls suggesting that residual stress may be present inthe cast specimen

Both the machined surfaces were found to have low RMSvalues, with more than 98% of all the points measured lyingwithin a range of ±0.1 mm Larger values were considered to

be outliers, particularly those occurring at the calcified sutures.This close visual and dimensional resemblance to the digitalmodel agrees with the results reported by Da Costa [26].The RMS T value measured for the gypsum mold (coreand cavity) was slightly lower than that observed for themachined PMMA surfaces The small difference can beattributed to the greater stiffness afforded by the gypsumand the aluminum flask As milling was done after thesetting time had elapsed, no significant expansion of thegypsum was expected, contrary to what is observed when

an implant is molded in gypsum slurry [27]

The casting time was longer than that recorded for directmachining, as it includes the time required for tasks related

to the production of the mold as well as the molding phaseitself The process times shown in Tables 4 and 5 are thesum of the setup or run times for each task in the sequence

in which they are carried out However, as preparation ofthe gypsum and casting of the PMMA block can be carriedout beforehand, both the cast and direct-machining processtimes can be shortened to 7.5 and 4.7 h, respectively.Layer-manufactured patterns are extensively used toproduce molds for cast PMMA implants [1,7] D´Urso et

Table 2 Results of the dimensional error assessment of the different

Gypsum mold (cavity) 0.314 −0.141 0.022

Gypsum mold (core) 0.462 −0.090 0.028

External (machined) 0.986 −0.373 0.043

Internal (machined) 0.637 −0.175 0.045

Table 3 The bounding box dimensions for the manufactured

specimens and the STL model

External surface Directions

Machining the mold cavity 40 100

Machining the circular groove 10 5

Table 5 Setup and run times for the main direct-machining tasks Task Setup time (min) Run time (min)

Machining the sacrificial material 30 25 Machining the concave surface 15 65 Machining the convex surface 70 80

al [1] reported an average time of 10 h to produce casting

patterns and 4 h to cast the PMMA implant

However, despite the time saving afforded by the

processes investigated here, these cannot compete with

layer-based technology when the implant geometry is

highly complex, particularly when the implants contains

hollow regions and small cavities

The main goal of this work was achieved Despite their

complexity, the PMMA specimens produced were

mea-sured and the dimensional differences for each specimen

were determined Compared with direct machining, casting

implies a longer lead time and larger dimensional deviation

However, if a proper offset value is adopted in the

mold-design phase, shrinkage can be minimized Accordingly, the

inherent advantages of casting, such as the possibility of

producing implants made of composite materials, as

proposed by Schiller et al [11], can compensate for the

longer lead time associated with this technique

Acknowledgments The authors would like to express their gratitude

to CAPES, the Brazilian Agency for Postgraduate Education.

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ORIGINAL ARTICLE

Multi-objective optimization of green sand mould system

using evolutionary algorithms

B Surekha&Lalith K Kaushik&Abhishek K Panduy&

Pandu R Vundavilli&Mahesh B Parappagoudar

Received: 2 May 2010 / Accepted: 25 April 2011 / Published online: 7 May 2011

# Springer-Verlag London Limited 2011

Abstract The quality of cast products in green sand

moulds is largely influenced by the mould properties, such

as green compression strength, permeability, hardness and

others, which depend on the input (process) parameters

(that is, grain fineness number, percentage of clay,

percentage of water and number of strokes) This paper

presents multi-objective optimization of green sand mould

system using evolutionary algorithms, such as genetic

algorithm (GA) and particle swarm optimization (PSO) In

this study, non-linear regression equations developed

between the control factors (process parameters) and

responses like green compression strength, permeability,

hardness and bulk density have been considered for

optimization utilizing GA and PSO As the green sand

mould system contains four objectives, an attempt is being

made to form a single objective, after considering all thefour individual objectives, to obtain a compromise solution,which satisfies all the four objectives The results of thisstudy show a good agreement with the experimental results.Keywords Green sand mould system Optimization Genetic algorithm Particle swarm optimization

1 IntroductionDuring moulding process, the quality of the parts produceddepends on the properties (that is, green compressionstrength, permeability, hardness and bulk density) ofmoulding sand It is important to note that improper levels

of these properties leads to common casting defects, such asblow holes, pinhole porosity, poor surface finish, dimen-sional variation, scabs and rat tails, misruns, etc It is alsoimportant to note that the mould properties are influenced

by a large number of controllable parameters (that is, grainfineness number, percentage of clay, percentage of waterand number of strokes) Hence, it is important to identifythe levels of the input variables that provide required mouldproperties, which improves the quality of the partsproduced by this mould

Most of the research work on moulding sand during1960s and 1970s was based on experimental and theoreticalapproaches The relationship between permeability andtransformation zones, mould pressure, void space control,etc., was developed by Marek [1] through substantialmathematical equations In addition to this, Frost and Hiller[2] established the pressure and hardness distributions insand moulds Later on, Wenninger [3] utilized the rigidwater theory to explain sand–clay–water relationships Thisapproach was completely theoretical and not supported by a

B Surekha:P R Vundavilli ( *)

Department of Mechanical Engineering,

DVR & Dr HS MIC College of Technology,

Kanchikacherla, Andhra Pradesh 521180, India

e-mail: panduvundavilli@gmail.com

B Surekha

e-mail: surekha_vundavilli@yahoo.co.in

L K Kaushik:A K Panduy

Department of Mechanical Engineering,

Rungta College of Engineering & Technology,

Bhilai, Chattisgarh 490024, India

Department of Mechanical Engineering,

Chhatrapati Shivaji Institute of Technology,

Durg, Chattisgarh 491001, India

e-mail: maheshpg@gmail.com

DOI 10.1007/s00170-011-3365-8

large number of experiments Moreover, statistical design

of experiments (DOE) had been used by various

inves-tigators to study the effects of different variables on the

green sand mould properties In [4], DOE technique was

applied to study the effect of process variables on bulk

density and green compression strength In addition to these

approaches, Casalino et al [5] utilized Taguchi technique to

establish third order model for permeability and

compres-sion strength in laser sintered sand moulds Moreover,

Parappagoudar et al [6,7] developed linear and non-linear

statistical models utilizing full factorial DOE, central

composite design (CCD) and Box-Bhenken design In the

above work, the authors had considered grain fineness

number, percentage of clay, percentage of water and

number of strokes as input parameters and green

compres-sion strength, permeability, hardness and bulk density as the

responses Among the non-linear regression equations

developed by the abovementioned three approaches,

CCD-based model was found to be the more accurate

model for prediction of the responses Later on, the

optimization of process parameters of green sand casting

was established in [8] utilizing Taguchi parameter design

The process parameters such as green compression

strength, moisture content, pouring temperature and mould

hardness vertical and horizontal were considered to identify

the effect of these parameters on casting defects As the

above developed method involved a traditional

optimiza-tion method, the soluoptimiza-tions obtained were not global optimal

in nature Therefore, a global optimization method is

required to identify the optimal combination of parameters

for achieving the desired performance of the green sand

mould system In single objective optimization, one

attempts to obtain the best design or decision, which is

usually the global maximum or minimum depending on the

optimization problem In green sand mould system, it is

difficult to find a single optimal combination of parameters

for green compression strength, permeability, hardness and

bulk density Hence, there is a need for a multi-objective

optimization method to arrive at the solutions to this

problem This multi-objective optimization problem can

be converted to a single objective problem after applying a

suitable method

This type of problems can be best solved by utilizing

evolutionary algorithms, such as genetic algorithms (GA),

particle swarm optimization (PSO), etc The early use of

evolutionary search was first reported in the 1960s by

Rosenberg [9] Since then, there had been a growing

interest in devising different evolutionary algorithms for

multi-objective optimization There exist two general

approaches to solve the multi-objective optimization

prob-lem The first approach deals with combining individual

objective functions into a single composite objective

function after assigning weight to each objective The

second approach is based on generating Pareto optimalsolution sets that are non-dominated with respect to eachother The present research falls in to the first category.Evolutionary optimization approaches, such as GA andPSO, have attracted a great deal of attention in recenttimes With their better global search abilities, theseoptimization approaches can find global optima morequickly through cooperation and competition among thepopulation of potential solutions It is important to notethat GA was used to solve multi-objective optimizationproblems related to grinding [10], turning [11, 12],abrasive flow machining [13], wire electric dischargemachining [14], drilling and riveting sequence planning[15], etc Similarly, PSO was also used to solve theproblems related to grinding [10], electrochemical machining[16], steam temperature control [17] and some otherengineering problems [18] To the best of the authors’knowledge, not much work had been reported in the field ofmulti-objective optimization of process parameters of greensand mould systems

In the present paper, the non-linear regression equationsdeveloped in [7] has been considered for multi-objectiveoptimization, utilizing the most popular evolutionaryalgorithms, such as GA and PSO Green compressionstrength, permeability, hardness and bulk density areconsidered as responses (that is, objectives) and grainfineness number, percentage of clay, percentage of waterand number of strokes are treated as inputs (that is, processvariables) A single objective has been formed aftercombining the four responses Both the GA and PSOalgorithms are used to optimize this single objective toobtain a solution It is interesting to note that the resultsobtained by these algorithms are comparable

The rest of the manuscript is organized as follows.Section 2deals with the formulation of the problem Toolsand techniques used in this study are explained in Section3.The results are presented and discussed in Section4 Someconcluding remarks are made in Section 5

2 Formulation of the problemThe quality of the parts produced in green sand mouldsystem mainly depends on the properties (responses) ofthe mould, such as green compression strength (GCS),permeability (P), hardness (H) and bulk density (BD),which in turn depends on the process variables (that is,grain fineness number, percentage of clay, percentage ofwater and number of strokes) Figure 1 shows theschematic diagram of green sand mould system as aninput–output model

The ranges of the process variables used in this study aregiven in Table1

While conducting the experiments [7], sieve analysis test

was conducted to determine the grain fineness number and

size distributions of the silica sand Moreover the strength

of clay was obtained by performing gelling index test

Then, the experiments were conducted with different

combinations of parameters using central composite design

Finally, the responses, such as permeability, green

com-pression strength, hardness and bulk density, were

mea-sured The relationship between the responses and the

process variables available in the abovementioned literature

were as given below:

In the present research, an attempt is being made tooptimize the process with multiple mould performanceoutputs utilizing evolutionary algorithms A weightedmethod is used for the said purpose Since the GCS, P, Hand BD are the four different objectives, in order toovercome the large differences in numerical values betweenthe objectives, the function corresponds to every mouldperformance output is normalized Then weighted method

is adapted to the normalized performance outputs to form asingle objective function Hence, the resultant weightedobjective function to be maximized is:

Maximize Z ¼ wð 1 f1þ w2 f2þ w3 f3þ w4 f4Þ ð5ÞSubjected to constraints:

Table 1 Process parameters and their ranges

% of water (% W)

% of clay (% C) No.of strokes (NS)

Permeability (P) Green compression strength (GCS) Hardness (H)

Bulk density (BD)

Fig 1 Input and output

variables of green sand

moulding system

where f1, f2, f3 and f4 are the normalized functions for

GCS, P, H and BD, respectively Moreover,w1,w2,w3andw4

are the weighted factors for the normalized GCS, P, H and

BD, respectively, and A, B, C and D are the process variables

It is important to note that the weighted factors are selected in

such a way that their sum will be equal to one A higher

weighing factor for an objective indicates more importance to

that particular objective Five different cases have been

considered after choosing different values for the weights: case

1:w1=0.25, w2=0.25, w3=0.25 and w4=0.25; case 2: w1=

0.70,w2=0.10,w3=0.10 andw4=0.10; case 3:w1=0.10,w2=

0.70,w3=0.10 andw4=0.10; case 4:w1=0.10,w2=0.10,w3=

0.70 andw4=0.10 and case 5:w1=0.10, w2=0.10, w3=0.10

andw4=0.70 These values are selected randomly in such a

way that the sum of the weights will be equal to one

3 Tools and techniques used

In the present work, evolutionary algorithms, such as a

binary coded GA and PSO, have been employed to

optimize the single objective function (refer to Eq 5) of

the green sand mould system The descriptions of these

algorithms are provided in the subsequent subsections

3.1 Genetic algorithms

Genetic algorithms are population-based search and

tion procedures, extensively used in the search and

optimiza-tion of various problem domains [10–15] The block diagram

showing the working cycle of a GA is shown in Fig.2 The

selection criterion used in the present study is tournament

selection, and uniform crossover is being used as crossover

mechanism Finally, bit-wise mutation is used to avoid the

local minima if any As there are four process variables, each

variable is represented with the help of ten bits Therefore,

40 bits are used to represent a GA string as shown below

3.2 Particle swarm optimizationPSO is a population-based stochastic optimization tech-nique Due to its easy implementation and quick conver-gence, PSO has gained much attention in solving manycomplex problems [9, 13,14] PSO algorithm is a modelthat mimics the movement of individuals in a group In thepresent study, MOPSO-CD [19], a variant of PSO has beenutilized for the selection of optimum process parameters ofgreen sand mould system The schematic diagram showingthe working cycle of the PSO is shown in the Fig 3 Thepresent approach incorporates the crowding distance (that

is, the average distance of its two neighbouring solutions)and mutation operators into the simple PSO algorithm Thisfeature enhances the exploring capability of the algorithm

by preventing the premature convergence problem of PSOalgorithm Instead of using evolutionary operators, such asselection and crossover, each particle in the populationmoves with velocity which is dynamically adjusted Thenew position and velocity of the particles have been

population of solutions Gen = 0 Randomly initialize

Is

? Gen > max gen

Stop

Assign fitness to all solutions

Reproduction

Crossover

Mutation Gen=gen+1

Start

No

Yes

Fig 2 Flow chart of the genetic algorithm

Source DF Sequenced SS Adjusted SS Adjusted MS F p F table

calculated using the formulation given below:

The new velocity :V i½ ¼ W  V i½ þ R1½Pbesti½ P i½

þ R2 A G½ ð bestÞ p i½ ð10Þ

The new position : P½ ¼ P i½ þ V i½ ð11Þ

whereW is the inertia weight, which is equal to 0.4, R1and

R2are the random numbers in the range of [0,−1], Pbest[i]

is the best population that the particleI reached and A(Gbest)

is the global best guide for each dominated solution The

parameters, namely swarm size, number of generations,

inertia weight (W), social components R1 and R2 and

repository size, play an important role in the present

approach

4 Results and discussion

The results of computer simulations carried out using GA

and PSO are discussed below

4.1 Genetic algorithms

A parametric study (that is, by varying one parameter of

GA, namely probability of crossover (pc), probability ofmutation (pm), population size and maximum number ofgenerations at a time) has been conducted to determine thecombination of GA parameters that are responsible for theoptimal mould performance It is also important to note thatthe selection of the weighting factor is also important, and itshould be selected based on the requirement of the decisionmaker In this study, five different cases (refer to Table 2)have been considered after varying the weighing factors ofthe objectives The results of the parametric study areshown in Fig 4, and the procedure for conducting thesystematic study is as follows

Figure4a shows the variation of fitness with change in theprobability of crossover (Pc) after keeping the probability ofmutation, population size and number of generations at afixed level As the problem to be solved is a maximizationproblem, the probability of crossover value (Pc*) whichproduced the maximum fitness has been chosen from thisstudy The variation of fitness with probability of mutation(Pm) is shown in Fig 4b In this study, the probability ofcrossover is set at Pc* and population size and number ofgenerations have been kept at the same level as given inFig 4a The probability of mutation (Pm*), which isresponsible for maximum fitness is identified Figure 4

shows the variation of fitness with population size (pop) Theprobability of crossover and mutation are kept at Pc* and

Pm*, respectively Moreover, the number of generations iskept at the same level as discussed in Fig.4a, b In this casealso, the population size (pop*) which is responsible formaximum fitness has also been identified Finally, the studyhas been conducted to determine the maximum number ofgenerations (gen*) that maximized the fitness (refer toFig.4d), after fixing the other parameters, such as probability

of crossover, probability of mutation and population size at

Pc*, Pm* and pop*, respectively The values of the GAparameters obtained by this study are as given below:probability of crossover (pc*)=0.85

probability of mutation (pm*)=0.18population size (pop*)=130max number of generations (gen*)=100Table 3 shows the optimum conditions of the mouldparameters for multiple performance outputs with differentcombinations of the weight factors Moreover, the maxi-mum fitness values for cases 1 to 5 are found to be equal to0.6654, 0.7620, 0.8522, 0.7629, and 0.6072, respectively.Case 3 is recommended because it gives maximum greencompression strength, moderate permeability, maximumhardness and maximum bulk density

Generate initial population

Evaluate fitness

Is optimal solution

Compute crowding distance

Update position & velocity

4.2 Particle swarm optimization

Here also, a systematic study has been conducted to

determine the swarm size, inertia weight and maximum

number of generations The study is conducted by varying

one parameter at a time It is important to note that small

swarm size may result in local convergence; large size will

increase computational efforts and may lead to slowconvergence The results of this study are shown inFig 5 The method of conducting the systematic study is

as follows:

Figure5a shows the variation of fitness with the change

in the values of inertia weight During this process, theother two parameters, such as swarm size and generations

Fig 4 GA parametric study: a

Pc vs fitness, b Pm vs fitness, c

population size vs fitness and d

maximum generations vs fitness

Table 3 Optimum mould parameters for multiple responses with different weighing factors using GA

Process parameters and responses Optimum values of mould parameters and responses

are kept at the fixed level In this case, the inertia weight

value (W*

), corresponding to the maximum fitness is

identified Figure5b shows the study related to the swarm

size In this case, inertia weight is set atW*

and maximumgenerations are set at the same level as discussed in Fig.5a

In this study, the swarm size (SS*) that is responsible formaximum fitness has been found Figure 5c shows theconvergence of the solution over number of generations.The number of generations (G*

) that are responsible formaximum fitness has been obtained in this study Thus, the

(c)

Fig 5 PSO parametric study: a

inertia weight vs fitness, b

swarm size vs fitness and c

maximum generations vs fitness

Table 4 Optimum mould parameters for multiple responses with different weighing factors using PSO

Process parameters and responses Optimum values of mould parameters and responses

parameters of PSO that are responsible for the better

performance are as follows:

inertia weight (W*

)=0.2swarm size (SS*)=50

number of generations (G*

)=30

In this case also, five different cases as mentioned in the

above approach have been considered after varying the

weight factors of the objectives Table 4 shows the

optimum conditions of the mould parameters for multiple

performance outputs with different combinations of the

weight factors It is interesting to note that the maximum

fitness values for cases 1 to 5 are found to be equal to

0.6647, 0.7609, 0.8719, 0.7630 and 0.6066, respectively In

this method also, case 3 is recommended, as it has produced

maximum fitness

4.3 Comparison between GA and PSO

While applying the evolutionary algorithms, such as GA

and PSO, to optimize a particular system, a number of

parameters are required to be specified The speed of

convergence of the algorithm depends on an appropriate

choice of the parameters The optimal parameters of GA are

found to be equal to probability of crossover 0.85,

probability of mutation 0.18, population size 130 and

maximum number of generations 100 In the case of PSO,

the optimal parameters of inertia weight, swarm size and

number of generations are found to be equal to 0.2, 30 and

50, respectively It is clear that the convergence rate of PSO

is faster than that of the GA Moreover, confirmation

experiments were conducted for the case 3 of both the GA

and PSO The percentage errors associated with GCS,P, H

and BD are found to be equal to 3.25%, 8.46%, 5.35% and

2.45%, respectively, for GA, and 3.22%, 5.78%, 4.95% and

2.25%, respectively, for PSO Moreover, the CPU times

required by GA and PSO, for a population size of 50 and

number of generations equal to 30, are found to be equal to

0.021 and 0.013 s, respectively, on a P-IV machine It is

interesting to note that PSO has performed better than GA

for all the responses This may be due to the simple

structure and minimal parameter tuning of PSO compared

to GA

5 Conclusions

In the present work, an attempt has been made to search for

the optimum process parameter values for the multiple

objectives, namely green compression strength,

permeabil-ity, hardness and bulk denspermeabil-ity, utilizing evolutionary

algorithms, such as GA and PSO It is interesting to note

that PSO has performed better than GA in terms ofcomputational efficiency Moreover, the percent deviationwith the experimental results for all the responses for PSO

is less than that of the GA The simple structure associatedwith minimal parameter tuning helps the PSO in out-performing the GA

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ORIGINAL ARTICLE

graded nano-composite ceramic cutting tools

Guangming Zheng&Jun Zhao&Zhongjun Gao&

Qingyuan Cao

Received: 12 February 2011 / Accepted: 3 May 2011 / Published online: 15 May 2011

# Springer-Verlag London Limited 2011

Abstract Sialon–Si3N4 graded nano-composite ceramic

tool materials were fabricated by using hot-pressing

technique The residual stresses in the surface layer of the

graded ceramic tool materials were calculated by the

indentation method The cutting performance and wear

mechanisms of the graded tools were investigated via

turning of Inconel 718 alloy in comparison with common

reference tools The surface roughness of the finish hard

turning of Inconel 718 and the microstructures of the chips

were also examined Worn and fractured surfaces of the

cutting tools were characterized by scanning electron

microscopy and energy-dispersive X-ray spectroscopy

The results showed that graded structure in Sialon–Si3N4

graded ceramic tool materials can induce residual

compres-sive stresses in the surface layer during fabrication process

Tool lifetime of graded ceramic tool was higher than that of

the common reference tool The longer tool life of the

graded nano-composite ceramic tool was attributed to its

synergistic strengthening and toughening mechanisms

induced by the optimum graded compositional structure of

the tool and the addition of nano-sized particles Wear

mechanisms identified in the machining tests involved

adhesive wear and abrasive wear The mechanisms

respon-sible for the higher tool life were determined to be the

formation of compressive residual stress in the surface layer

of the graded tools, which led to an increase in the

ceramics and Sialon ceramics limiting their applications aretheir relatively lower hardness and wear resistance [3,4].Conventionally, Si3N4and Sialon ceramic materials werestrengthened and toughened by the addition of particles likeSiC, WC, TiCN, TiC, etc to improve the mechanicalproperties [5–7] The matrix grains of the composite canalso be refined by adding nano-Si3N4particles, because theaddition of nano-Si3N4particles could promote the forma-tion of the duplex distribution characteristic The optimumflexural strength and fracture toughness were obtainedwhen the volume fraction ratio of nano-sized Si3N4 tomicro-sized Si3N4is fixed at 1:3 [8] The introduction ofthe concept of functionally graded material (FGM) into thefabrication of ceramic cutting tool materials provided a newapproach to improve their thermal and mechanical proper-ties [9, 10] Functionally graded cutting ceramics withsymmetrical structure have been developed to performhigh-speed intermittent machining of hard-to-cut materials,with higher tool lives than those of homogeneous cuttingceramics [11]

G Zheng ( *):J Zhao:Z Gao:Q Cao

Key Laboratory of High Efficiency

and Clean Mechanical Manufacture of MOE,

School of Mechanical Engineering, Shandong University,

The understanding of the wear mechanisms in cutting

processes is the prerequisite for not only the proper

application but also the development of graded ceramic

tool materials In turning of Inconel 718, the typical wear

types of ceramic tools are crater wear, flank wear, and depth

of cut notch wear which are sometimes accompanied by

chipping [12–14] The typical wear mechanisms of ceramic

tools in turning of Inconel 718 are adhesive wear, abrasive

wear, plastic deformation, diffusion wear, and

micro-breakout [1, 14,15] The wear mechanisms vary with the

location of worn area For example, adhesive wear is the

main wear mechanism in the rake face, while abrasive wear

is the main wear mechanism in the flank face The depth of

cut notch wear is very severe when machining Inconel 718

with ceramic tools [2, 16] Additionally, the diffusion

elements of Inconel 718 alloy to the tool rake face might

accelerate the tool wear rate [17]

In the present paper, Sialon–Si3N4 graded

nano-composite ceramic tool materials were fabricated by means

of the optimization of graded compositional structure of the

tool and the addition of nano-sized particles The cutting

performance and wear mechanisms of graded ceramic tools

were investigated via turning of Inconel 718 alloy, with

common reference tools used as competitors Worn surfaces

of the tools were characterized by scanning electron

microscopy (SEM) and energy-dispersive X-ray

spectros-copy (EDS) to reveal the wear mechanisms Surface

roughness of the finish hard turning of Inconel 718 andmicrostructures of the chips were also analyzed

2 Experimental method2.1 Preparation of Sialon–Si3N4graded nano-compositeceramic tool materials

Figure 1 shows the cube-shaped model of five-layeredgraded material with symmetrical structure The composi-tional distribution changes along the Z-axis The thickness

of the surface layer, the second layer, and the third layerwerea, b, and c, respectively And H is the total thickness

of the material, where H ¼ 2a þ 2b þ c Thickness ratio

e ¼ a=b ¼ b=c, a structural parameter, was defined todetermine the thickness of each layer The thickness ratio

is fixed ate=0.3 in the present work

Graded structures can be properly designed to induce asurface compressive residual stress The basic idea is tomodel material layers with different thermal expansioncoefficients so that residual stresses arise during thefabrication process Compressive residual stresses can beinduced in a layer with lower thermal expansion coefficients

In virtue of the thermal mismatch effect between the matrix

Si3N4(thermal expansion coefficientαmatrix=3.2×10−6K−1)and the TiC0.7N0.3 (αparticle=8.6×10−6 K−1), the layer withthe highest volume fraction of TiC0.7N0.3 was put in themiddle with the compositional distribution changing fromthe middle layer to the two surface layers Both the twoopposite surfaces of an insert made by this means could beused as rake face

The starting materials were α-Si3N4 powders withaverage grain size of approximately 0.02 and 0.5 μm,

Fig 1 Cube-shaped model of five-layered graded material with

symmetrical structure

Table 1 Composition (vol.%) of different composites

Composites Si 3 N 4 (0.5 μm) Si 3 N 4 (0.02 μm) Al 2 O 3 (0.5 μm) Al 2 O 3 (0.1 μm) AlN (0.5 μm) TiC 0.7 N 0.3 (0.5 μm) Y 2 O 3

purity 99% (Hefei Kai Nanometer Energy and Technology

Co., Ltd., China), TiC0.7N0.3 particles with average grain

size of approximately 0.5 μm, purity 99% (Beijing

Xingrongyuan Technology Co., Ltd., China), α-Al2O3

powders with average grain size of approximately 0.1μm,

purity 99.6% (Shanghai TeamShare Nanotechnology Co.,

Ltd., China) and AlN powders with average grain size of

approximately 0.5μm, purity 99% (Hefei MoK Advanced

Material Technology Co., Ltd., China) α-Al2O3 powders

with average grain size of approximately 0.5 μm, purity

99.9% (Zibo Xinmeiyu Alumina Co., Ltd., China) and

Y2O3(Sinopharm Chemical Reagent Co., Ltd., Shanghai,

China) were used as sintering additives to promote the

densification of Si3N4 ceramics during the sintering

process The composition of different composites is shown

in Table 1 For the composite SAAT10, β-Sialon phase

could be produced by chemical reaction between the major

phases Si3N4, Al2O3, and AlN [18]

The surfactant polyethylene glycol (Sinopharm Chemical

Reagent Co., Ltd., Shanghai, China) and ethanol were

used as dispersant and dispersing medium, respectively,

to obtain well-reagglomerated and uniform suspension of

Al2O3 nano-particles and Si3N4 nano-particles The

sus-pensions were then mixed with micro powders of the same

composite The mixed slurries were ball-milled for 48 h

and then dried at 100°C in vacuum (Model ZK-40, China)

The powder mixtures were sieved through a 120 mesh sieve

The composite powders with different mixture ratios were

layered into the graphite mold one layer after another with a

predetermined thickness ratio, according to the material

design results listed in Table 2 The specimens were then

sintered by a multifunctional hot-pressing sintering furnace

(Model ZRC85-25T, China) in a vacuum environment

(the working vacuity is 6.75 × 10−2 Pa), at temperature of

1,700–1,750°C for 60 min under a fixed uniaxial pressure ofP=35 MPa For the purpose of comparison, homogeneousreference ceramic materials (SAAT10 and ST10) were alsomanufactured by hot pressing

Flexural strength was measured by using a three-pointbending tester (Model WDW-50E, China) per internationalstandard ISO/DIS 14704: 2000 Fracture toughness mea-surement was performed using indentation method [19].Samples for fracture toughness and hardness testing wereindented with a Vicker’s hardness tester (Model HV-120,China) with a load of 196 N and a holding time of 15 s Aminimum number of five specimens were tested for eachcondition Indentation tests were performed on the topsurface of the outer layer and the residual stresses werecalculated [20,21] The calculation results of residual stress

in the material surface layer of GSS1 and GSS2 are −492and −442 MPa, respectively

2.2 Cutting experimentsThe cutting performance of the Sialon–Si3N4graded nano-composite ceramic tools (GSS1 and GSS2) were tested incomparison with that of the reference ceramic tools(SAAT10 and ST10) and a commercially available Sialonceramic tool (Kennametal, type is SNGN120408T01020,grade is KY1540) The mechanical properties of the fivetool materials are listed in Table 3 The tools have thefollowing geometry parameters: rake angleγ0=−5°, clearanceangleα0=5°, inclination angleλs=0°, and side cutting edgeangleκr=45°

A computer numerically controlled center lathePUMA200MA with maximum spindle speed of 6,000 r/minwas used for the machining trials Machining trials wereconducted using 120 mm diameter × 380 mm long Inconel

Table 3 Averages and standard deviations of mechanical properties of the tool materials

Tools Flexural strength ( σ f , MPa) Fracture toughness ( K IC , MPa m1/2) Vicker ’s hardness (HV, GPa)

a The property of the surface layer

Table 4 Chemical composition of Inconel 718 (wt.%)

0.031 0.18 0.05 51.50 <1.0 0.0032 19.16 0.05 0.58 0.97 3.07 Bal 0.0055 0.0057 5.06

718 alloy bars The chemical compositions and mechanical

properties of Inconel 718 workpiece materials used in the

experiments are given in Tables4 and 5, respectively All

tests were carried out with the following parameters: depth of

cut ap= 0.1 mm; feed rate f=0.1 mm/rev; and cuttingspeed v=80, 120, and 200 m/min

Average flank wear VBav e= 0.30 mm was used as thetool life criterion The wear condition of the cutting edgewas examined periodically, and any apparent change in theedge surface was closely examined with an opticalmicroscope A scanning electron microscope (JSM-6380LA, Japan) equipped with an energy-dispersive X-ray spectrometer was used to examine the nature of theworn tools and observe morphological features of chips.The surface roughness of the Inconel 718 was measured

Fig 3 SEM micrographs of etched fracture surface, a the first layer

of GSS1, b the first layer of GSS2

Fig 2 Average flank wear curves of the five tools at f=0.1 mm/r and

a =0.1 mm, a v=80 m/min, b v=120 m/min, c v=200 m/min

Table 5 Mechanical properties of Inconel 718

Yield strength (MPa) Tensile strength (MPa) Elongation (%) Reduction of area (%) Hardness HRC

by a portable surface roughness tester (Model TR200,

China) The surface roughness measures used in the

paper is the arithmetic mean value of the surface

roughness of profile, Ra

3 Results and discussion

3.1 Cutting performance of Sialon–Si3N4graded

nano-composite ceramic tools

Figure2 shows the variation of average flank wear width

with cutting time of the five tools in turning of Inconel 718,

tested at different speeds with 0.1 mm/r feed rate and

0.1 mm depth of cut As can be seen from Fig.2a–c, among

the five cutting tools, graded nano-composite ceramic tools

(GSS1 and GSS2) showed better performance than that of

homogeneous reference tools (ST10 and SAAT10),

espe-cially at lower cutting speeds 80 and 120 m/min

Furthermore, the graded ceramic tool GSS1 showed better

cutting performance than that of the commercially available

Sialon ceramic tool KY1540 at lower cutting speeds 80 and

120 m/min The tool life of SAAT10 was the shortest

because of its relatively low flexural strength and fracture

toughness (see Table3)

The cutting experiment results showed that tool life was

affected by cutting speeds significantly The ceramic tools

exhibited a relative long cutting life at cutting speed

120 m/min As can be seen from Fig 2a, the ceramic

tools were not suitable for low speed cutting of

nickel-based alloys because of the higher cutting force of the five

tools at cutting speed 80 m/min, at least under this

experiment conditions The increase in cutting speed

caused a larger increment in cutting temperature at the

cutting edge of the tools The higher temperature caused

the tools to lose their strength Therefore, the tool lives

were shortest at cutting speed 200 m/min (Fig.2c)

Figure 3a and b show the SEM micrographs of etchedfracture surface of the first layer of GSS1 and GSS2 Thefractured specimen surfaces for SEM observations wereeroded in melting NaOH at 400°C for 1 min As shown inFig 3, the matrix grains of the composite were refined bythe addition of nano-Si3N4 particles In virtue of thedifference in Si3N4grain size, there existed a dissolution–transport–reprecipitation gradient in the material after theformation of the liquid phase This led to the formation ofthe interlocked duplex microstructure of β-Si3N4 grains(Fig 3a) and β-Sialon grains (Fig 3b) The interlockedduplex microstructure contributed much to the improve-ment of flexural strength and fracture toughness Inaddition, the graded structure in ceramic tool can also lead

to a little increase in fracture toughness and hardness at thesurface layer of graded tools (see Table 3) The higherfracture toughness and hardness of graded tool can result inthe decrease in the tool wear rate This effect may beanother reason for the increase in flank wear resistance ofgraded tools over the homogeneous ones Therefore, thelonger tool life of the graded nano-composite ceramic toolshould be attributed to its synergistic strengthening andtoughening mechanisms induced by the optimum gradedcompositional structure of the tool and the addition ofnano-sized particles

The surface roughness is one of the measures forevaluation of the product accuracy and plays an importantrole in predicting the capability of machining performance

It is a significant surface quality index that is known tohave considerable influence on properties such as wearresistance and fatigue strength of a component The cuttingspeed is one of the influencing factors on the surfaceroughness [22,23] Figure4shows the relation between thesurface roughnessRaand the cutting speed In the range ofcutting speed from 80 to 200 m/min, the value of surfaceroughness Radecreased with the increase of cutting speed.The cutting force reduced with the increase of cutting speed

Fig 5 Effect of average flank wear on surface roughness at v=120 m/ min, f=0.1 mm/r, and a =0.1 mm (GSS1)

Fig 4 Effect of cutting speed on surface roughness at f=0.1 mm/r

and a p =0.1 mm

in high-speed machining process According to dynamics

theory, the cutting force is the main source of vibration

excitation in cutting process As the cutting speed increases,

the operating frequency of the cutting system is away from

the low-level natural frequency of the machine Furthermore,surface roughness is greatly sensitive to the low-level naturalfrequency So high-speed machining can significantly reducethe surface roughness

Fig 6 Wear patterns of the five tools in turning of Inconel 718 tested at v=120 m/min, f=0.1 mm/r, and a p =0.1 mm, a ST10 tool (after 6.5 min),

b GSS1 tool (after 7.8 min), c SAAT10 tool (after 4.0 min), d GSS2 (after 4.7 min), e KY1540 tool (after 6.4 min)

As can be seen from Fig.5, the tool wear has also an

effect on the surface roughness in the cutting process At

the initial wear stage of cutting process, there was a wear-in

process between the insert and the workpiece And there

may be some burrs and unstable factors (e.g., micro-cracks)

at the tool surface So the surface roughness was higher

during the initial cutting stage At the steady wear stage, the

fluctuation of cutting force was small The relative stable

cutting process contributed to the lower surface roughness

At the rapid wear stage, the higher surface roughness was

attributed to the rapid flank wear which led to the increase

of cutting force

3.2 Wear mechanisms of Sialon–Si3N4graded

nano-composite ceramic tool

The SEM observations of the five tool faces after the

turning of Inconel 718 at 120 m/min cutting speed are

shown in Fig.6a–e It can be clearly seen that besides craterwear and flank wear, the depth of cut notch wear for ST10,GSS1, and SAAT10 took place However, tool wearoccurred only in the area of the tool nose radius cornerfor KY1540 due to its larger tool nose radius (0.8 mm) andthe selection of a small depth of cut of 0.1 mm (the ratio ofthe tool nose radius to the depth of cut rε/ap=8) Andchipping was found for KY1540 tool, which might beattributed to its relatively lower fracture toughness than that

of other tools (see Table 3) As can be seen from Fig.6cand d, significant notch wear can be observed for SAAT10tool, while the GSS2 graded tool was less sensitive to thiswear mode

The wear characteristics of the rake and flank faces ofGSS2 after machining at 120 m/min for 4.7 min are shown

in Fig.7 High stresses generated at the tool–chip interfaceduring machining may also cause plastic deformation alongthe chip flow direction (Fig 7a) It also can be seen thatthere are some abrasive traces (point 1 in Fig.7a and point

3 in Fig.7b) and built-up layer (point 2 in Fig.7a and point

4 in Fig 7b) on both the rake and flank faces This wearbehavior is typical for abrasion and adhesion

On the one hand, at the initial wear stage and the steadywear stage, the built-up layers deposited on the tool faceand could reduce the deterioration of some grooves or pitsduring cutting, hence they had a protective action, whichinhibited tool wear and improved tool life On the otherhand, at the rapid wear stage, some elements of theworkpiece which might spread to the ceramic tool increasedthe affinity between the chip and tool material and thusaccelerated the tool wear rate EDS analyses of point 2 inFig 7a is shown in Fig.8 Point 2 is very enriched in Ni,

Cr, and Fe, which are the elements of Inconel 718 alloy (see

Fig 7 SEM images of worn faces of GSS2 tool in turning of Inconel

718 tested at v=120 m/min, f=0.1 mm/r, and a p =0.1 mm (after

4.7 min), a rake face, b flank face

Fig 8 EDS analyses of the point 2 in Fig 7a

Table4) There was experimental evidence of diffusion of

Ni element of Inconel 718 alloys to the tool material, whichwas reported by Deng et al [17] Ni, Fe, and Cr may lowerthe hardness of the ceramic tool surface because of its lowmelting point So the adhered elements of Inconel 718 alloy

on the tool rake face such as Ni, Fe, and Cr may acceleratethe tool wear rate

As calculated above, compressive residual stresses can

be introduced at the surface layer of the graded tools duringthe fabricating process (cooling from the sintering temper-ature to room temperature) That is to say the differences inthermal expansion coefficient of the layers are sufficient toinduce residual compressive stresses in the surface layer.The tools in machining process are subjected to severestress, which may cause surface micro-cracks, and thefailure of the tools always depends on the stress distribu-tion Once the maximum tensile stress exceeds the tensilestrength of the tool material, fracture will occur However, a

Fig 9 Magnification of nose wear region of GSS1 in turning of

Inconel 718 tested at v=120 m/min, f=0.1 mm/r, and a p =0.1 mm

(after 7.8 min)

Fig 10 SEM micrographs of serrated chip when machining

Inconel 718 nickel-based alloys with GSS1 graded ceramic

cutting tool at f=0.1 mm/r and a p = 0.1 mm, a is the top view of

chip at v=80 m/min, b is the top view of chip at v=120 m/min, c is the top view of chip at v=200 m/min, and d is the side view at v=

200 m/min

part of the tensile stress which was generated during cutting

process at the surface layer can be counteracted by the

compressive residual stresses at the surface layer for the

graded tools The propagation of a crack generated in the

surface layer can be considerably hindered because these

compressive residual stresses may decrease in the

crack-driving force Although there was a micro-crack in the

nose wear region (Fig 9), severe fracture cannot occur

when the tool was failure Therefore, the resistance to

fracture at the surface layer is attributed to these

compressive residual stresses

In many cases, the abrasive wear may also be attributed

to special features of the flowing chip which is

character-ized by a serrated profile along its edges (Fig 10) This

type of serrated chips abrades the tool rake face and creates

scars in the rake wear surface As can be seen from

Fig 10a–c, the deformation of the chip was increasingly

serious with the increase of the cutting speed from 80 to

200 m/min The tendency of serration of the chip increased

with the increase in cutting speed As can be seen from the

chip of the side view (Fig.10d), the chip was very highly

serrated atv=200 m/min The formation of serrated chips

results from a catastrophic thermoplastic shear This

phenomenon takes place when thermal softening in the

primary shear zone, due to high heat generation during

high-speed machining, is equal or higher than the strain

hardening produced by high strain rate This leads to the

formation of the shear bands In addition, the low thermal

conductivity of the workpiece hinders the evacuation of the

heat generated during the cutting process, resulting in a

temperature rise of the workpiece High cutting temperature

and heat often result in severe plastic deformation Typical

serrated chip is formed under higher cutting speed (Fig.10c

and d) when workpiece thermal softening becomes more

effective than strain hardening

In high-speed turning of Inconel 718 alloy, the tools

suffer from severe abrasion, adhesion, and depth of cut

notch wear, which can accelerate the tool wear rate The

longer life of the graded nano-composite ceramic tool

should likely be ascribed to its synergistic strengthening

and toughening mechanisms induced by the optimum

graded compositional structure of the tool at macro-scale

and the addition of nano-sized particles at micro-scale

However, future theoretical research is needed to validate

the longer life of the graded nano-composite ceramic tool

and to improve the design of tool material

4 Conclusions

Sialon–Si3N4graded nano-composite ceramic tool

materi-als with high mechanical properties were hot pressed The

cutting performance and wear mechanisms of graded

ceramic tools were investigated via turning of Inconel 718alloy in comparison with common reference tools Thefollowing conclusions can be deduced from the findings ofthis study

1 Tool life of the graded ceramic tool was higher than that ofthe common tools The longer tool life of the graded nano-composite ceramic tool was attributed to its synergisticstrengthening and toughening mechanisms induced bythe optimum graded compositional structure of the tooland the addition of nano-sized particles

2 Wear mechanisms identified in the machining testsinvolve adhesive wear and abrasive wear The mecha-nisms responsible for the higher tool life weredetermined to be the formation of compressive residualstress in the surface layer of the graded tools, which led

to an increase in resistance to fracture

3 The surface roughness decreased with increasing thecutting speed And the surface roughness was lower atthe steady wear stage than that of other wear stages Inaddition, it was also found that the chip morphologywas serrated type under higher speed cutting condition

Acknowledgments This research is supported by the National Basic Research Program of China (2009CB724402), the National Natural Science Foundation of China (50875156), and the Specialized Research Fund for the Doctoral Program of Higher Education (20090131110030).

References

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Received: 19 November 2010 / Accepted: 8 May 2011 / Published online: 24 May 2011

# Springer-Verlag London Limited 2011

Abstract Nowadays, approaches in chatter detection and

control are based on chatter prediction, by using a

machining system dynamic model, or on chatter detection

by different techniques, but after chatter onset They are not

efficient because the models are complicated and specific

(in the first case) respectively because of chatter unwanted

consequences occurrence (in the second case) This paper

presents a method for early detection of the process

regenerative instability state (as a specific process current

dynamical state), based on cutting force monitoring Using

the cutting force records, the process current dynamical

state is assessed Appropriate cutting force signal features

are defined, based on signal statistic processing, signal

chaotic modeling or signal harmonic analysis, and used on

this purpose The process dynamical state evolution is

modeled aiming the features values prediction Two types

of models were used in this purpose: linear and neural The

instability regenerative mechanism is identified by using

either dedicated features or input variable selection The

method was conceived and experimentally implemented in

the case of turning process The results show the method

reliability and the possibility of using it in developing an

intelligent system for stability control

Keywords Regenerative instability Early detection

Predictive control Logistic model Turning

1 IntroductionChatter, more or less obvious, inherently affects the cuttingprocess It may lead to negative effects concerning surfacequality, cutting tool life, and machining precision Thechatter does not mean every time instability, but it isfrequently a source to initiate the regular regime of periodicvibrations, which is unacceptable, because it might totallycompromise the machining system performances or it mightaffect its integrity

A significant and increasing number of researchesconcerning chatter were carried out during recent years,aiming at a better understanding of cutting processdynamics and to find efficient tools for avoiding the chatteronset during the machining process They were focusedmainly on three topics: (1) chatter prediction, (2) chatterdetection, and (3) chatter control

If referring to chatter prediction, new models of thedifferent machining processes (turning, milling, drilling,and grinding) were successively developed to overcome thelimits of the classical theory concerning the cutting processstability [1] They generally involve time delay differentialequations, which after being solved enable to draw specificstability charts, depending on machining system particularproperties A well-known 1-df nonlinear model to describeregenerative cutting tool chatter in turning or milling wassuggested by Hanna and Tobias [2] Moon and Kalmar-Nagy [3] developed a model that includes hysteretic effectsvia a constitutive relation, linear in stress, rate of stress,strain, and strain rate Insperger et al [4] and Mann et al.[5] investigated the dynamic stability of the milling processusing a single-degree of freedom model Two alternativeanalytical methods were introduced, both based on finitedimensional discrete map representations of the governingtime-periodic delay-differential equation Stability charts

G R Frumu şanu (*):A Epureanu:I C Constantin

Manufacturing Science and Engineering Department,

“Dunărea de Jos” University of Galaţi,

Gala ţi, Romania

e-mail: gabriel.frumusanu@ugal.ro

Int J Adv Manuf Technol (2012) 58:29–43

DOI 10.1007/s00170-011-3383-6

and chatter frequencies were determined for partial

immer-sion up- and downmilling, and for full immerimmer-sion milling

operations Stability prediction for milling was also

approached by Gradisek et al (2-df model), Totis

(proba-bilistic algorithm), Song et al., Sivasakthivel et al (response

surface methodology), Damir et al., Wan et al (unified

stability prediction method for milling process with

multiple delays) [6–11] Specific tools were also developed

and applied in order to study the milling process stability [12,

13] An overview of chatter modeling in metal cutting and

grinding was realized by Altintas and Weck [14] In [15],

Campbell and Stone presented a nonlinear model to analyze

drilling process instability Chatter prediction in turning

processes, without an explicit model of the problem domain

was performed by Khorasani et al [16] by using case-based

reasoning and also by Cardi et al [17] by using ANN

The interesting results and significant progresses obtained

in various research domains (chemistry, physics, medicine,

meteorology, etc.) by implementing Chaos theory encouraged

its application in studying the cutting process dynamics [18]

Litak recently analyzed the intermittent vibrations in a

regenerative cutting process, based on a chaotic 1-D model

[19] In [20], Johnson and Moon used Poincaré sections,

phase plane portraits and false nearest neighbor techniques to

understand bifurcations in the lathe cutting of aluminum The

experiments presented offer evidence of deterministic tool

vibrations below the classical onset of chatter By applying

elastic, dissipative, and inertial properties to a plain oscillator,

excited by the cutting force components, Wiercigroch

developed a bidimensional chaotic model of the machine

tool–cutting process system [21]

As a conclusion, concerning the aforementioned models

and chatter prediction methods, almost all of them are built

starting from theoretical considerations and their

applica-bility is then investigated by numerical analysis and

comparison to the real situations Up to now, none of them

was implemented in a reliable technical system for

controlling the machining systems stability

For chatter detection, sensors and data acquisition system

are firstly used for process monitoring (no matter if talking

about acceleration, cutting force, torque, worked piece/

cutting tool displacement, sound pressure) Whatever being

the monitored signal is, the important thing is its processing

technique, which should be based on defining, evaluating,

and analyzing appropriate feature vectors Thus, Messaoud et

al [22] performs chatter detection in drilling process using

multivariate control charts, while Tansel et al [23] uses

Index Based Reasoner for chatter detection in end milling,

both tools being applied to the torque signal S transform is

used for chatter detection in turning, if applied to the

acceleration signal [24], or in drilling, if applied to the

displacement, velocity, torque, and thrust force signals [25]

Wavelet technique is a modern tool also used for on-line

chatter detection in turning, by analysis of ultrasound waves[26], in boring, combined with support vector machine [27],

in milling, based on probability distribution of waveletmodulus maxima [28] or in grinding [29] More than that,Yao defines a parameter computed on the base of vibrationacceleration signal [27] and claims to anticipate the chatteronset with a time advantage of about 1 s However, the way

it looks in this moment, this detection technique is hard to beused in practice Kuljanic et al [30] present the development

of an intelligent chatter detection system in milling; thestatistical parameters obtained from wavelet decomposition

of the acceleration signal were used to detect chatter by using

an artificial classification system based on neural networks.Tangjitsitcharoen [31,32] conceived an in-process monitoringand identification method of cutting states in turning Itutilizes the power spectrum density (PSD) of dynamic cuttingforce measured during cutting On the base of experimentalresults, it is stated that there are three basic types of PSDpatterns corresponding to continuous chip formation, brokenchip formation and chatter Another similar method [33],grounded on pattern-recognition technique, consists in calcu-lating three parameters by using the ratios of the dynamiccutting forces average variances to classify the alreadymentioned cutting states Several techniques for chatterdetection by audio signal processing were also developed:Nair et al [34] (based on permutation entropy), Schmitz et al.[35], (using signal variance as chatter indicator), Weingaertner

et al [36], and Tsai et al [37] Soliman and Ismail [38]present a method to detect the instability by monitoring thecurrent absorbed by spindle driving system

It must be noticed that, with very few exceptions, thechatter is detected after its onset, too late for enabling anefficient intervention on the cutting regime in order to avoidunwanted consequences At the same time, it is difficult tospecify a proper threshold for many of the chatter indicessince they are affected by the permanently changingconditions of the cutting process

As regards the chatter control there are, in principle, twoapproaches:

– Offline control, based on stability charts (chatterprediction) and consisting in choosing the cuttingregime such as the system operating point is placedunder the stability limit;

– Online control, based on chatter detection followed bychatter suppression, either by changing the cutting regimeparameters (most often meaning, in fact, their diminution)

or by applying other technical solutions: cutting speedvariation [39–41] cutting tool oscillation [42]

In the first case, a possible modification of the relativeposition between the system operating point and thestability limit, which might be caused by different factors,cannot be considered For this reason, the cutting regime

must be set to safe values, meaning a lower productivity

than it should be possible to perform In the second case,

the control is made after chatter onset while the chatter

structure (its components and their weight) is ignored

In this paper, it is considered the case when the self-excited

vibration is the chatter main component (which is frequent) A

method for online early detecting the regenerative instability,

before the process becomes unstable with negative

conse-quences, is presented In this purpose, appropriate cutting

force signal features are defined and evaluated online They

are used for assessing the process current dynamical state

Feature value evolution is modeled in order to predict the

future process dynamical state This could be further used to

conceive an intelligent control system aiming to keep the

machining system operating point as close as possible to the

stability limit in order to maximize the productivity

The paper is structured as follows The next section is

focused on problem formulation In the third section, the

principle of the new developed method for early detection

of the regenerative instability is described In the fourth

section, an experimental program performed in order to

exemplify the new method application, in turning, is

presented Numerical results generated by implementing

the method are exposed and discussed Finally, there is a

section dedicated to conclusions

2 Problem formulation

Here, by chatter we will expressly mean the cutting edge–

worked piece relative displacement, which cumulatively

satisfies the following three conditions:

1 It comes out in addition to the motion programmed

through the part program and this is the reason why it

represents a deviation from the tool programmed path

2 Its magnitude rapidly varies in time, following a

quasi-periodic law

3 It is exclusively caused by the cutting force variation

(after a similar law) or, in other words, if the cutting

force does not vary, this motion disappears

The cutting tool–worked piece relative displacement,

taking place during the interrupted cutting, is categorized as

chatter only if it satisfies the (b) condition from above; such

an example is in the milling case

Chatter can be distinguished from other cutting tool–

worked piece relative motions, like the following:

– Forced vibration caused by unbalanced rotating parts—

because it is not the effect of the cutting force variation

– Parametric vibration, induced by periodic variation of

the machining system stiffness—which also takes place

when the cutting force remains unchanged

– Free vibration—when the excitation force is null, themotion energy being injected in the system by shock.There are two main sources of the chatter:

1 The cutting process basic instability as intrinsicphenomenon Its main cause is the chip formationdiscontinuous process, proved by the chip variablesection although its provenience material layer hadconstant section and the cutting speed does not change.The material sliding along the shear direction is notcontinuous but intermittent and leads to the chipelements specific form It appears even when themachining system stiffness is very high, withoutallowing any noticeably elastic deformation of themachining system

2 The perturbation regeneration, which induces what wewill call“regenerative instability” It has two regenerationmechanisms The first one comes to the fore through acausal dependence between the previous cutting sequence(the cause) and the current cutting sequence (theeffect), as successive sequences The second regenerationmechanism works when the cutting tool successivelypasses by the same point, from a cutting cycle to the nextcutting cycle For example, in turning a cutting cyclemeans a complete rotation of the worked piece Theywill be respectively referred as“primary regenerativeinstability” (regeneration between successive sequences)and “secondary regenerative instability” (regenerationbetween successive cutting cycles) The two mechanismscan act simultaneously This is the reason why thevibration motion resulting from the regenerative insta-bility can be considered as having two components,generated by these mechanisms and combining theireffects

Because the two components of the regenerativeinstability stand on completely different regenerationmechanisms, their suppression/diminishing methods arealso different

From practical point of view, chatter detection is alwaysperformed aiming chatter control The actual chatterdetection/control techniques have the following generaldrawbacks:

– They detect only the instability onset without givingany indication about the evolution of the machiningprocess referred to the stability limit

– The chatter control strategy associated to the actualdetection techniques consists in modifying the position ofthe system operating point, until it rejoins the stabledomain, usually meaning a reduction of the cutting processintensity Here, by system operating point, we mean thetriplet v (cutting speed), s (feed rate), t (cutting depth)characterizing the cutting regime at a given moment

– The reaction at vibration onset is undifferentiated (as

mechanism) because the detection does not refer to

vibration regeneration mechanism

– Both the machining system operating point and the

stability limit change during the cutting process

because of the detached material thickness variation,

material properties non-homogeneity, tool wear

evolu-tion, machining system dynamical characteristics

change, etc As consequence, the relative position

between them also changes between broad limits This

is happening in each one of the following situations:

& When the tool moves along the programmed path

& When changing a sample by another one from the same

manufacturing batch

& When passing from a type of part to another type of part

machining

Because it is impossible to anticipate most of the

aforementioned events, the instability may suddenly

appear The solution of avoiding this by programming a

low enough cutting regime is not suitable from an

economic point of view

The objective of this paper is to develop a method

for early detection of the regenerative instability The

method should meet the following requirements:

– To give information about the distance between the

process current dynamical state and the process

regenerative instability state

– To predict the future evolution of the distance between

the aforementioned states

– To indicate the weight of the two regeneration

mechanisms inside the vibration regeneration process

– To enable its online application

– To be simple, in order to give a rapid evaluation of the

process current dynamical state

– To not require calibration before its use (or, at least, to

To develop the new method we firstly had to choose the

cutting process parameter which should be monitored

online, in order to reflect the process current dynamical

state There are two options for such a parameter: the

cutting force variation or the cutting tool–worked piece

relative motion

On the one hand, as chatter means a motion exclusively

caused by the cutting force variation and, reciprocal, there

is no chatter if the cutting force is constant, we mayconclude that between chatter and cutting force variationthere is a bi-univocal relation On the other hand, we mayalso accept that, during the chatter regeneration process, thecurrent cutting force oscillation is a delayed consequence ofits variation at the previous rotation of the worked piece, atthe previous cutting force oscillation or both Becausechatter is the expression of the cutting tool/worked piecerelative motion, appearing as consequence of the cuttingforce variation, we chose the cutting force as characteristicparameter to detect the regenerative instability by onlinemonitoring

It must be noticed that by cutting force, in general, wemean any one among the cutting force components (maincomponent, thrust force, and feed force) or any othersystem-internal force, if only it satisfies the condition to bedirectly dependent to the cutting force

Another necessary remark concerns the sampling quency during online measurement of the cutting force,which must be up to 10 times higher than the chatterfrequency The reason for such a requirement is to ensure asufficient number of points for the cutting force signalprocessing

fre-Finally, the result of the process online monitoringconsists in continuously recording of the cutting forcevalues This record must be organized as successive timeseries in order to enable an appropriate processing.Regarding time series length, each one corresponds to aworked piece complete rotation Everyone among them isthen divided in the same number of sequences (e.g., 4, 6 or

8, depending on the worked piece diameter and rotationspeed)

3.2 Signal processing

In this purpose, firstly, the features that could be furtherused in process current dynamical state assessment and inmain regeneration mechanism identification must be de-fined Then, the cutting force signal should be processed, inorder to find the features values

1 Features for the process current dynamical stateassessment

If we look at graphical representations of the cuttingforce signal, there are big differences between theaspects corresponding to stable versus unstable processcurrent dynamical state (Fig 1 a, b) After examiningmany pictures of this kind and putting them inconnection to the corresponding dynamical state, wechose a number of cutting force signal features that can

be used to detect early the regenerative instability Theyare concerning the regenerative instability effects—theappearance, in the cutting force variation map of

whisks including, in the initial stages, a number of 10–30

oscillations with periodic aspect These features are

referred to a signal sequence of given length and can be

grouped on three categories, as follows

(a) Features based on signal statistic processing

I1 The number of peaks (local extreme points)—a

smaller number of peaks corresponds to a dynamical

regime closer to periodic

I2 The average jump, between two successive local

peaks—its increase will also show an imminent

transition to vibration

I3 The average time interval between two successive

peaks—if it is greater, the system is closer to its

stability limit

(b) Features based on signal chaotic modeling

Several tools developed within Chaos theory to

analyze system dynamics, can also be used to

define features for characterizing the cutting

process current dynamical state, in order to early

detect the regenerative instability These features

are the following:

I4 The largest Lyapunov exponent According to the

Chaos theory, the largest Lyapunov exponent can

characterize the dynamics model When the dynamics is

described through time series, consisting in measured

values of a characteristic parameter, the largest Lyapunov

exponent,l1, can be calculated with the relation [43]

lndjðiÞ

where, Δt is time series sampling period, dj(i) the

distance between the jth pair of the nearest neighbors

after i discrete time-steps, M the number of structed points A positive largest Lyapunov exponentdiagnoses chaotic dynamics, while a null exponentmeans a perfect periodic dynamics If analyzing cuttingforce time series of same length, then a Lyapunovexponent steady decreasing tendency could indicate thestability limit nearness

recon-I5 The logistic model fitness, meaning the standarddeviation of the difference between measured cuttingforce time series having a given length and its fittestlogistic model The logistic model is a well-known 1-Dseries, given by the law

xn¼ r  xn1ð1  xn1Þ; ð2Þwhere, r means the control parameter If its values arerestricted to the range 0≤r≤4, then relation (2) mapsthe interval 0≤×≤1 into itself

I6 The control parameter of the fittest logistic model(as upper defined) If r takes values close to 4 orsmaller than 3.2, then the series long time behavior isaperiodic, while if its values are between 3.2 and 3.6,then the logistic model map looks periodic

(c) Features based on signal harmonic analysisThe discrete Fourier transform (DFT), applied

to a cutting force time series and computed with afast Fourier transform algorithm, can be used inorder to find the frequency components of thevibration caused by the machining process regen-erative instability Starting from here, anotherindicator for early detecting the regenerativeinstability can be adopted, namely

I7 The ratio between the maximum and the meanamplitude of signal DFT, in a given frequency domain.Fig 1 Force signal variation depending on process current dynamical state: a stable, b unstable

If I7is close to 1, then the process is stable; otherwise,

it reflects the neighborhood of the stability limit

2 Features for main regeneration mechanism identification

Because the two components of the regenerative

instability stand on different regeneration mechanisms

and the corresponding control strategies are also

differ-ent, their weight inside the vibration regeneration

process must be assessed A comparison should be

made in this purpose between sequences from the

cutting force-measured signal in order to find possible

correlations Here by sequences, we mean successive

oscillations, in the case of searching for primary

regenerative instability or oscillations sets from

succes-sive cutting cycles if looking for the secondary

regen-erative instability In this purpose, the following features

were considered:

I8 The maximum specific value of the

autocorre-lation function The autocorreautocorre-lation function A is

very large time interval compared to the dominant

periods in the motion Hence, I8is defined as

I ¼ 1 Að Þt

T

RT

f meaning, in our case, the cutting force If I8takes

values near to 1, the cutting force signal

autocor-relation is very good, while values near to 0 mean

the primary regenerative instability absence

I9 The correlation coefficient will be used to give

a measure of the correlation between two sets of

the cutting force values having the same length

and registered for two successive cutting tool

passes through the same worked piece region; in

statistic, its notation is rxyand it is determined by

using the formula

rxy¼

1

n

Pn i¼1ðxi mxÞ y i my

Here, x and y are two sets of n points each,μx,

μy—their average values and σx,σy—their standard

deviations If rxy is closer to 1, the correlation

between the two sets is better When referring to the

cutting force signal, I9magnitude directly depends

on the secondary regenerative instability presence.The main regenerative instability mechanism(MRM) will be then determined by calculating aratio, I10, between the maximum specific value ofthe autocorrelation function and the correlationcoefficient (characterizing the same dataset),

I10 ¼I

If I10is greater than 1, MRM is the regenerationfrom the previous cutting sequence to the currentcutting sequence (primary regenerative instability isstronger); if I10is smaller than 1, then MRM is theregeneration from the previous cutting cycle to thecurrent cutting cycle and the secondary regenerativeinstability prevails

3.3 Early detection of the regenerative instabilityThe early detection of the regenerative instability can beperformed by predicting the values of the cutting forcesignal features and comparing them to specific thresholdvalues To predict a feature value requires the modeling ofthe process dynamical state evolution Regarding thetypology of the model that could be used on this purpose,preliminary tests showed two types of models as the mostsuitable, namely a linear model (when only small experi-mental datasets are available) and a neural model (in thecase of larger datasets)

For modeling the process dynamical state evolution, first

of all, the successive time series of feature recorded values,divided into equal sequences, are put together into a featuremap, as it can be seen in Fig 2 Here, each row means aworked piece complete revolution, and each rectangle—acutting force signal sequence, characterized by a featurevalue

The current window in this map includes m rows(corresponding to the last m rotations of the worked piece,before the current moment) constituting the dataset that isused for model updating and another l rows (the next

l rotations) defining the domain where the currentlyupdated model will be used for prediction As themachining process goes ahead, rotation after rotation, thecurrent window does the same thing

We now consider the current (i, j) feature value, from thefeature map ith row and jth column By keeping in view thetwo perturbation regeneration mechanisms, this value can

be determined on the base of a model, having as inputvariables the feature previous values in horizontal direction(i, j-1), (i, j-2)…and the feature previous values from the

same column (i-1, j), (i-2, j)… Because we do not know,

from the beginning, which among the input variables are

the most significant for building the model, an input

variable selection must be performed, for example by using

the dedicated facilities of the Best Neural Network Modelsoft This selection gives important information about themain regeneration mechanism: if the selected variables arefrom the same line i, then we have primary regeneration;while if they are from the same column j, there is secondaryregeneration; a mixed selection (line–column) shows bothmechanisms presence at comparable levels

MRM gives the precise meaning of the“early detection”:

it refers to predicting the feature value for the next sequence(i, j+1; primary regeneration case, Fig 3a) or the featurevalues for the next entire worked piece revolution (secondaryregeneration case, Fig.3b)

We also investigated the possibility of realizing an earlierdetection of the regenerative instability, by performing aprediction domain extension to a higher number ofsequences/rotations This can be done by including thefeature values resulted from a first-stage prediction in thedataset used for a second-stage prediction of the featurevalues Thus, a new model updating can be performedbefore going ahead from the current sequence andrecording new values of the cutting force feature Themodel updated on such a manner can further be used forextending the early detection domain by making a newprediction The procedure may be reiterated and the weight

of feature predicted values in the dataset graduallyincreased until arriving to a model entirely built on thebase of predicted values It must be noticed that, obviously,the higher the number of predicted feature values used formodel updating is, the poorer the prediction performance

Current sequence (i,j)

Dataset used for model updating

Fig 2 The feature map

Unchanged model area

Dataset used for model updating

1 q-m

q

q+l-1

i, j

Fig 3 Feature value modeling and prediction: a primary regeneration mechanism, b secondary regeneration mechanism

becomes However, the results obtained by such a prediction

domain extension could be used, up to a given limit, in order

to enable a feature prediction time advantage at least equal to

the time needed by a stability control system for reacting

The following modeling process parameters are in play

for predicting the feature values:

– The dimension of the dataset used for model updating

(the number m of previous rotations considered)

– The unchanged model area (the number l of rotations to

apply the updated model)

– The number of times of model updating, between two

consecutive settings of the modeling process parameters, p

– The number of nearest neighbors, K, from the

K-nearest neighbors algorithm

The algorithm for modeling and predicting the feature

value is presented in Fig 4 and includes the following

typical actions:

(a) Initial setting of the modeling process parameters,

meaning default values for m, l, K, and p

(b) Initial model building, which means:

& Linear model as default;

& MRM found on the base of I8and I9features;

& Construction of the input variables dataset by assigning

to each recorded sequence the corresponding input

variables values;

& Finding the K-nearest neighbors cluster using the

current sequence as pivot;

& Fitting on this cluster a linear model of type

feature i; jð Þ ¼ a  feature i  1; jð Þ þ b  feature i  2; jð Þ;

ð7′Þor

feature i; jð Þ ¼ a  feature i; j  1ð Þ þ b  feature i; j  2ð Þ;

ð7″Þ

depending on MRM, namely (7′) for primary or(7″) for secondary

(c) Current model updating, which means:

& Neural model as default;

& Construction of the learning matrix;

& Input variables selection using Best NN Model and,

on this base, establishment of the main regenerationmechanism;

& Neural model training

(d) Optimisation of the modeling process, meaning to findthe best new values for the modeling processparameters m and l In this purpose, the optimisationcriterion is the prediction performance, assessed by themean, ε, and the standard deviation, σ, of thedifference between features measured and predictedvalues This can be done by running the model inorder to predict feature values already known and bycomparing then the predicted values to the real ones

4 Experiments4.1 Experimental setup

An experimental program has been performed in order totest the new developed method for early detection of theregenerative instability The peripheral surface of a flange-type worked piece from regular steel (0.45% C) and havingthe initial diameter of 350 mm was successively machined

by turning on a NC transversal lathe A Sandvik Coromantcutter, PSSNR2525M12 type, with removable inserts ofSNMG120416KM type was used

The cutting force main component, Fz, was monitored.Its value was measured by using a Spider 8 data acquisitiondevice (Hottinger Baldwin Messtechnik), in connectionwith two strain gages applied in half-bridge on the cutter.The data accounting frequency was of 9,600 scans persecond and the results were recorded

m m

m

(c) Actions (a), (b)

Action (c)

Actions (c), (d) Action (c)

Actions (c), (d) Action

(c)

Fig 4 Algorithm for modeling

and predicting the feature value

Table 1 The experimental cutting regimes

Fig 5 The cutting force variation map

Table 2 The cutting force variation and the features values in the first window sequences

4.2 Experimental plan

The experimental plan was conceived aiming to reach the

following objectives:

(a) Analysis and preliminary selection of the features used

for current dynamical state assessment

(b) Analysis of the techniques used for main regeneration

mechanism identification:

& Evaluation of dedicated features (I8, I9), during the

initial stage of the regenerative instability early

(d) Assessment of the method performance as regards the

magnitude of the prediction domain extension

Three series of experiments were realized by successively

increasing the depth of cut t from 1.5 to 2.5 mm and then to

4 mm In each of these three cases, an experiment consisted

in turning by giving to the cutting tool a 4 mm displacement

in radial direction, with a different rotation speed (meaning,

in fact, a different cutting speed); the feed rate remained

constant, s=0.22 mm/rot All the experimental cutting

regimes are presented in Table1, where D means the initial

surface diameter, n the worked piece rotation speed, and vthe corresponding cutting speed

An essential requirement concerning the experimentswas to create the possibility of finding the angular position

of each measuring point around the worked surface in order

to retrieve the machining process dynamical behavior whenthe cutting tool successively passes more times through thesame place Our solution for this problem was to use arotation-inductive transducer, giving one impulse for everyworked piece revolution, registered simultaneously to thecutting force signal

Fig 6 The cutting force variation and features values in the case of the second window

Table 3 Prediction performance depending on dataset dimension m (linear model, l=2, K=2)

5 Results

The experimental plan was implemented in the cases

denoted in Table 1 They are corresponding to situations

when the cutting process took place in the stability limit

proximity and they were found by examining all the cutting

force variation diagrams, in connection with the generated

surfaces aspect If referring to the cutting force signal

aspect, the appearance of whisks, including a certain

number of periodic oscillations (usually 10–30) and having

unnecessarily high amplitude, compared to the rest of the

signal, is specific At the beginning, the whisks are

separated by large intervals of cutting force variation

characteristic to the free vibration (see the cutting force

variation map in Fig.5) If the machining system operating

point is getting closer to the stability limit, the number of

whisks increases, they include more oscillations and their

amplitude rises Once the stability limit overtaken, the

whisks merge between them at vibration onset and the

entire force signal becomes periodic

We further present the results obtained by implementing

the experimental plan in the case of the sample turned by

68 rev/min from the second series of experiments During

this experiment, the worked piece completed 14 rotations;

corresponding to them, a cutting force variation map was

drawn (Fig.5) It resulted by aligning in vertical direction

cutting force time series, each one including the cutting

force values recorded during a complete rotation; the first

section is the lowest, while the last is the highest Thus, it is

easier to analyze the process dynamics—for example, we

can remark the secondary regenerative instability evolution

between successive cutting cycles

The possibility to detect the regenerative instability by

using the dedicated features of the cutting force signal,

defined as above, is investigated In this purpose, the two

windows marked on the cutting force map were considered

The first one includes the cutting force values for the same

set of 931 points (the same sequence j), during the eighth,

ninth, 10th, and 11th rotation (i=8, 9, 10, 11) and it wasused to study the secondary regenerative instability evolu-tion The second window contains a higher number ofvalues (3,000) from the 11th rotation (i=11) in order toinvestigate the primary regenerative instability

In the case of the first window, the I1–I9features valueswere calculated for all the set of points; in the second case,the section of 3,000 points was cut in six equal sequences,the I1–I8features values being determined for each amongthese sequences The results are presented in Table 2 andFig 6, respectively

The I4 values were calculated by using an originalalgorithm, by considering in (1) the reconstruction delay as

4, the embedding dimension as 2 and the number ofdiscrete time steps as 5 The rest of the features wereassessed by using dedicated MatLab algorithms

Five cutting force signal features (I1, I2, I3, I5, and I6)were preliminarily selected by analyzing the concordancebetween features maps and cutting force map The firstand the last time series from the cutting force map wereeliminated, in order to avoid the influence of thetransitory effects caused by cutter ingoing/outgoing inthe cutting process The remaining 12 time series wereeach divided in eight equal sequences and the values ofthe selected features were calculated for all the 96 resultedsequences

The new method for regenerative instability earlydetection was then experimentally tested on the base ofthese data The conditions for testing the method were thefollowing:

& Selected features: I1, I2, I3, I5, and I6;

& The model type: linear and neural;

& The modeling process parameters values: dimensions ofthe dataset used for model updating, m=4, 5, and 6;unchanged model areas, l=2, 3, and 4

& The prediction domain extension: 1–5 worked piececomplete rotations (in the case of I6feature)

Table 4 Prediction performance depending on unchanged model area

dimension l (linear model, m=5, K=2)