Machining of High Strength Steels With Emphasis on Surface Integrity by air force machinability data center_2 docx

Normally in many previous testing programs, an uncoated cemented car-bide P20, or P10 grade would have been used, since these grades withstand both higher speeds and have better tool wea

Figure 146 A turning and boring surface texture test piece

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Figure 147 Machinability testing utilising an ‘accelerated testing procedure’ – a combination of the rapid facing and degraded

tool tests

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on a moderately short timescale Normally in many

previous testing programs, an uncoated cemented

car-bide P20, or P10 grade would have been used, since

these grades withstand both higher speeds and have

better tool wear resistance to that of previously utilised

cutting tool materials However in this case, an P25

grade was chosen, which is a degradation from the

optimum P20 grade, but it should still perform

satis-factorily Furthermore, the cutting speed was raised by

>2.5 times the optimum of 200 m min–, with all

fac-ing operations befac-ing conducted at a ‘constant surface

speed’ of 550 m min–

Typical tool-life curves produce by the AWT

tech-nique are illustrated in Fig 148, showing the expected

three stages of flank wear This flank wear being a

func-tion of: the initial edge breakdown, steady-state wear –

as the insert’s flank progressively degenerates and

fi-nally, catastrophic insert edge breakdown – as the edge

completely fails Detailed metallurgical analysis can be

made as to the reasons why some P/M compacts

per-formed better than others, by reference to the

litera-ture on the metallurgical interactions between the tool

and the compact – this subject being outside the scope

of the present discussion The facing-off secondary

machining operation meant that after 10 facing passes,

a pre-programmed ‘optional stop’ can then be applied,

to allow both tool flank wear and compact surface

tex-ture to be established The faced-off surface textex-ture

re-sults can then be superimposed onto the same graph –

for a direct comparison of flank wear and for that of

the machined surface texture parameter Without

go-ing into too much detail of the specific aspects of the

processing and metallurgical interactions present here

on the composite graph, some compacts abraded the

cutting insert more than others, while the ‘faced’

sur-face texture, generally seemed to get worse, then

im-prove and finally worsen again However, this is a

complex problem which goes to the ‘heart’ of the

vi- ‘Constant surface speed’ , this can be achieved by employing

the appropriate ‘canned-cycle’ G-code accessed from the CNC

controller, which allows the testpiece’s rotational speed to

in-crease as the faced diameter dein-creases*.

* Normally there is a restriction on the rotational speed limit

– created by the maximum available speed for this machine

tool, which would normally be reached well before the cutting

insert has coincided with that of components centre line, but

because in this instance, the compacted testpiece is hollow, the

rotational restriction does not present a problem.

sual aspect of machined surfaces – wherein the real situation is that surface texture continuously degen-erates, and it is only the burnishing (i.e.‘ironing’) of the surface that ‘masks’ the temporary improvement

in machined surface – more on this topic will be made

in the surface integrity section What is apparent from using the AWT technique is that on a very short tim-escale, considerable data can be generated and applied research assessments can be conducted both speedily and efficiently This topic of exploiting the minimum machining time and data-gathering activities to gain the maximum information, will be the strategic mes-sage for the following dialogue

Machinability Strategies: Minimising Machining Time, Maximising Data-Gathering

Prior to commencing any form of machinability tri-als, parameters for cutting data need to be ascertained

in order to minimise any likelihood of repetition of results, while reducing the amount of testpieces to be machined to the minimum Data obtained from such trials must be valid and to ensure that the cutting pa-rameters selected are both realistic and significant a

disciplined experimental strategy based upon the ‘De-sign of Experiments’ (DoE) approach is necessary – see

Fig 149 Here, a flow-chart highlights the step-by-step approach for a well-proven industrial technique,

to maximise the labour-intensive and costly exercise

of obtaining a satisfactory conclusion to an unbiased and ranked series of machinability results There are

a range of techniques that can be utilised to assess whether the cutting data inputs, namely: feeds, speeds,

DOC’s, etc., will result in the correct inputs to obtain

an extended tool life, or an improvement in the ma-chined surface texture from the testing program One

such method is termed the ‘Latin square’ – which

as-sesses the significance of the test data and its

interac-280 Chapter 7

Figure 148 Graphical results obtained from the accelerated machinability test, illustrating how flank wear and

surface texture degrades, with the number of facing-off passes

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tions For a practical machinability trial employing a

‘Latin square’ , it uses a two-way ANOVA table, with a

limited amount of ‘degrees of freedom’ , typically:

fee-drate, cutting speed, DOC, plus surface finish – these

parameters can be changed/modified to suit the

‘pro-gramme of machining’ in hand By using a very

lim-ited group of cutting trials, a two-way ANOVA table

can be constructed and their respective ‘F-ratio’ for

each interaction can be determined This calculated

‘F-ratio’ should be greater than the 5% ‘confidence limit’

of the statistical distribution to be significant If the

F-ratio falls below –5% (i.e for the calculated F-F-ratio),

then the interactions are not significant, which

ne-cessitates increasing the ‘factor strength’ (e.g

increas-ing the: cuttincreas-ing speed, feedrate, etc.), to generate data

which is >5% confidence limit – as shown by the

‘feed-back loop’ in Fig 149, or alternatively, using a different

factor By such means, ANOVA tests for significance of

machining data, ensures that the processing parameters

utilised for the prospective machinability trial are both

valid and the correct ones to use in the proposed

ma-chining programme

 ‘Analysis of variance’ (ANOVA), or as it should be more

ap-propriately termed the ‘analysis of variation about the means’ ,

consists of portioning the total variation present in a data set

into ‘components’ Each ‘component’ is attributed to an

iden-tifiable cause, or source of variation; in addition, one

‘com-ponent’ represents the variation due to uncontrolled factors

and random errors associated with the response

measure-ments.Specifically, if the data set consists of ‘n’ measurements

‘y.…,yn’ and their mean is denoted by: ‘y ’ , the total

varia-tion about the mean is embodied in the ‘sum of squared

de-viations’ , as following diagram depicts, for the ‘partitioning

scheme’ for ANOVA:

Total Sum of Squares about the mean:

n

�

i=(y− ¯y)

Sum of

squares

– due to

Source1

Sum of

squares

– due to

Source2

Sum of squares – due to Source3

Sum of squares – due to Source4

Error, or residual Sum of Squares

The technique of analysis of variance decomposes this total

‘sum of squares’ into the parts shown above, for a case in

which four identifiable sources of variation are present – in

addition to the ‘error component’ The number of identifiable

causes of variation and the formulae for the ‘component sums

of squares’ are intrinsically connected to the specific

experi-mental design utilised, in the data collection and to the

statis-tical model deemed appropriate for this analysis.

Rather than spending considerable time, effort

and indeed exorbitant expense, on a large and com-plex machining testing programme, which more often

than not, produces numerous machined components

that are almost indistinguishable from each other It might be more prudent, to conduct a ‘condensed’ series

of trials, based upon a rigorous statistically-designed

methodology Therefore, experiments based on the so-called ‘orthogonal arrays’ can be beneficially engaged

in this regard Many applied researchers and engineers have utilised a range of factorial-designed experi-ments, typified by the ‘Taguchi-approach’

The main problem with these ‘arrays’ is that in many situations the large number of ‘interactions’ (i.e fac-tors) have been shown to interfere with the overall re-sults – introducing ‘secondary effects’ , which will not have been anticipated for, when the original strategic programme was devised Such spurious data, could seriously affect future machining recommendations and influence the outcome in a negative manner The

‘interaction problem’ can have these affects consider-ably reduced by incorporating a more ‘truncated-ap-proach’ to the experimental design strategy for the machinability trials, rather than using a ‘full’ Taguchi orthogonal array (Fig 150) For example, if all of the experiments are conducted in for example one of ‘stan-dard’ the Taguchi L8(2) orthogonal array, depicted in Fig 150, then the ‘total outcomes’ (i.e components machined), would be: 2 = 128 × 8 = 1,024 individual components machined Here, in the Taguchi orthogo-nal array seven factors have been employed and with the vast amount of components produced from such a long-running and very costly machining programme, many of the pertinent details will be lost on those en-gineers/researchers attempting to de-code the vast as-sortment of machinability data collated However, it

is possible to utilise a much simpler-approach to the overall massive data-collection and analysis problem, yet still providing statistical significance, this can be

achieved by adopting a ‘Fractional factorial-designed experiment’ Here, instead of the virtually ‘mindless

task’ of producing 1,024 almost identical components,

 ‘Orthogonal array factors’ – when utilising a ‘full’

Taguchi-designed orthogonal array for a complete picture of all of the interactions, then it has been shown (Shainin, 1985 – see refer-ences), that if many factors are employed (i.e normally >5), this results in unwanted ‘secondary effects’ which cannot be accounted for, leading to spurious results from any

machin-ability trials

282 Chapter 7

by using a ‘Fractional factorial-designed experiment’

with an identical matrix to that given in Fig 150,

only 8 components are produced! This testing regime

is both significantly quicker and much less costly to

perform, obtaining a ‘snap-shot’ of the overall

ma-chinability problem, but because considerably less

tes-tpieces are produced, the ‘interaction-problem’ and its

‘secondary effects’ are not an issue, even when seven

factors are utilised Obviously, this machinability data

has to be collated and investigated in a disciplined and

controlled fashion One tried-and-tested method of

establishing an unbiased and ranked interpretation of

these results, is to use the much misunderstood and

maligned technique of ‘Value Analysis’ (VA) This VA

when used to show trends in competitive functions

 ‘Value Engineering and Analysis’ (VE/VA), with VE being

principally concerned with an overall improvement of design-based details on engineering components, while a more lim-ited form of this technique is termed VA – being particularly relevant for detailed interpretation of recorded data from ex-perimentation Here, in this case, from the wide-ranging and often seemingly unrelated output of machinability trials

Figure 149 Flow chart indicating the desigh philosophy for unbiased and ranked machinability trials

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Figure 150 A fractional factorial-designed experiment, based upon a Taguchi L8(27) – orthogonal array

284 Chapter 7

and operations, can be successfully utilised from the

comparisons of cutting fluids, through to complex and

difficult-to-machine aerospace machinability trials If

a more sophisticated technique is required, then it is

also possible to utilise ‘Quality Function Deployment’

(QFD), to obtain a complete picture of the outcomes

from machining trials QFD is often used by

indus-try as a means for its ‘Continuous-improvement

pro-grammes’ Here for ‘simplicity’s-sake’ , the more basic

and somewhat less complex VA tabulated

data-colla-tion approach, will be briefly reviewed

The application of VA to a series of collated and

compiled massed-data is not new In fact, it was

widely-used during the 1960’s, but fell into disfavour,

partly because its function and operation were often

not well-defined – this being exacerbated by poor

im-plementation of its recommendations However, VA

techniques are useful, allowing one to interpret data

trends both quickly and objectively – without undue

bias – at a glance of a spreadsheet Not only can

signifi-cant trends be readily seen, but the spreadsheet shown

in Fig 151 – shows a typical machinability data for P/

M compacts drilled by two differing drill-point

geom-etries By using the spreadsheet, not only can overall

trends be readily seen, it also can depict sub-set trends

as well, giving a complete picture (i.e globally) of the

important criteria in assessing machining data As a

simple ranking system is used, considerable

objectiv-ity can be gained and with little undue influence – bias,

affecting the outcome from these tabulated results In

employing the ranking of the results, it is normal

prac-tice to decrement down and if two values are ranked

identically, then they are given the same rankings,

fol-lowed by the next lower ranking, being two numbers

lower, as following example shows:

 ‘Quality Function Deployment’ (QFD), is a general term that

means the: ‘Deployment of quality through deployment of

qual-ity functions’ (Akao, 1988) It is often known as the ‘House of

Quality’ , because the tabulated graphical representation looks

similar to that of a house – when all the interacting factors

for subsequent analysis have been included on the chart This

QFD technique, is a wide-ranging philosophy for the

com-plete analysis of both simple and intricate designs and can be

successfully exploited for machinability trials.

 ‘Continuous-improvement programmes’ , can be defined as

an: ‘Operational philosophy that makes the best use of resources

in order to increase product, or service quality and result in

more effective satisfaction of customers’ (Swanson, 1995).

For example, in Fig 151 – for the values shown in column two (i.e left-hand side: Jobber drill, Thrust Force 0.254 N):

NB  Here, two 5’s were ranked, meaning that the next

decremented value would rank as 3 Hence, in this case

the Low compaction Compact type No 2 this was best and Low compaction No 4 worst – as jobber drilled.

This ‘truncated approach’ the elementary and easily comprehended VA tabulation (Fig 151) , enables non-specialists, together with knowlegdible experimenter,

to recognize the influence various machining param-eters have on the potential performance of the trials undertaken By judicious use, the VA technique in conjunction with a strictly controlled and limited ma-chining strategy – based upon some form of ‘orthogo-nal array’ , in combination with the ‘strength’ (i.e >5%

‘F-ratio’) of parameters by ANOVA, this will enable a researcher to conduct a speedy, compact, realistic, yet meaningful machinability assessment

7.2 Machined Roundness

Roundness is a condition of a ‘surface of revolution’ ,

which can take the form of a: cylinder, cone, or sphere, where all the peripheral data points (i.e measure-ments) intersect In reality, the radius of say, a nomi-nally round workpiece tends to deviate – from the

‘true circle’ – around the periphery of the part, making these variations the theme to subjective interpretation

of the measured results In fact, in the past, the sim-plistic technique for the assessment of roundness was usually measuring three diameters on a workpiece, to

determine the diametrical variations, then ‘averaging’

to give its overall dimensional size Moreover, for

vari-ations in a workpiece’s radius about an axis of rotation, this was often found by positioning the part between a

‘bench-’ , or sine-centres’ – the latter equipment is em-ployed for turned tapered features, then rotating and monitoring it with dial gauges both at and along its length In the past, this rather superficial metrologi-cal workpiece assessment was supposed to inform the inspector as to its potential in-service performance

If some radial variations occurred, this geometrical

Figure 151 Value analysis – tabulation of the performance of two drilling

points and a typical range of drilling data, when machining powder metal-lurgy compacts

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286 Chapter 7