Computational Intelligence In Manufacturing Handbook P9

Capable of generating the best process plan for a given part based on available machining resourcesin the shop floor.. The machines used include conventional machines e.g.,horizontal and

Zhang , Y F et al "Application of Genetic Algorithms and Simulated Annealing in Process "

Computational Intelligence in Manufacturing Handbook

Edited by Jun Wang et al

Boca Raton: CRC Press LLC,2001

9 Application of Genetic

Algorithms and Simulated Annealing

in Process Planning

Optimization9.1 Introduction

9.2 Modeling Process Planning Problems

in an Optimization Perspective

9.3 Applying a Genetic Algorithm

to the Process Planning Problem

9.4 Applying Simulated Annealing

to the Process Planning Problem

to be able to provide the following supporting roles to design and manufacturing:

1 Capable of conducting manufacturability assessment on a given part and generating modificationsuggestions for the poor design features

2 Capable of generating the best process plan for a given part based on available machining resources

in the shop floor The term “best” refers to a plan that satisfies a predetermined criterion

3 Capable of generating alternative process plans to suit production scheduling needs and/or changes

in the shop floor status

Over the last 20 years, there have been numerous attempts to develop CAPP systems for various partsmanufacturing domains, such as rotational and prismatic parts The levels of automation among thereported systems range from interactive, variant, to generative [Alting and Zhang, 1989] The discussion

in this chapter focuses on the generative type as it is the most advanced and preferred In general, themain characteristics of those reported generative CAPP systems, in terms of various decision-makingactivities in process planning, can be summarised as follows:

1 Machining features recognition — The workpiece, both the raw material and finished part, isgenerally given in a solid model representation The difference between the finished part and theraw materials represents the volumes that need to be removed The total removal volumes areextracted and decomposed into individual volumes, called machining features (e.g., holes andslots), which can be removed by a certain type of machining process (e.g., drilling and milling)

2 Operations selection — For each machining feature, an operation or a set of operations (e.g.,roughing and finishing operations) is selected

3 Machines and cutters selection — For each operation, a machine and a cutter are assigned to itfor execution, based on shop floor resources

4 Set-up plan generation — A set-up refers to any particular orientation of the workpiece on themachine table together with a fixture arrangement where a number of operations can be carriedout The tool approach direction (TAD) for each operation is determined A set-up is determinedbased on the commonality of TAD and fixture arrangement for several operations

Most reported CAPP systems carry out decision-making activities 2 through 4 in a linear manner, andeach is treated as an isolated deterministic problem (see Figure 9.1(a)) Such systems can only generate

a feasible process plan by going through one or several iterations of decision-making due to possibleconflicts among the different selection results As a result, they found little industrial acceptance, as theplans generated are far from optimal The main reasons behind this are

1 For each decision-making problem, the feasible solutions are most likely many but one Selectingonly one solution from each problem leads to premature reduction of the overall solution spaceand hence the optimal selection combination may well be lost on the way to the final solution

2 The decision-making problems are interrelated rather than independent of each other The lineardecision-making strategy may create various conflicts that result in many iterations

In conclusion, these decision-making problems must be considered simultaneously in order to achieve

an optimal process plan for a given part (see Figure 9.1(b)) Recently, the authors have noted that someCAPP approaches have been designed to tackle the problems mentioned above by partially integratingthe decision-making activities [Chen and LeClair, 1994; Chu and Gadh, 1996; Yip-Hoi and Dutta, 1996;Hayes, 1996; Gupta, 1997; Chen et al., 1998] However, full integration has not yet been achieved.When all the decision-making problems are considered simultaneously, process planning becomes acombinatorial problem Even for a problem with a reasonable number of machining features, it isimpossible to check every possible solution Efficient search algorithms must be developed to find theoptimal or near-optimal solution

In this chapter, a novel algorithm that models process planning in an optimisation perspective ispresented The application of genetic algorithms and simulated annealing to solve the process planningmodel are discussed together with a case study

9.2 Modeling Process Planning Problems in an Optimization Perspective

9.2.1 The Process Planning Domain

The process planning domain for a CAPP system is defined by the types of parts, machining features, andmachining environment it deals with The discussion focuses on prismatic parts The geometry of a part

is represented as a solid model created using a CAD software The raw material is assumed to be a machined prismatic block that just encloses the part (a convex hull) The basic machining features, whichcan be used to construct the most commonly encountered prismatic parts, are shown in Figure 9.2 Some

pre-of these simple features can be merged together to form complex intersecting machining features As forthe machining environment domain, a CAPP system should be flexible enough to handle common parts

in traditional job shops In this discussion, the job shop information in terms of its machining capabilityand current status is treated as user input through an open architecture, to update the currently availablemachining resources (machines, tools) along with their technological attributes such as the dimensions

FIGURE 9.1 Two typical CAPP approaches.

Operations? Machines?

Cutters? Fixtures?Set-ups?

Machining features

Machining features

Operations? Machines & Cutters? Fixtures & Set-ups?

(a) Process planning in a linear manner

(b) Process planning in a concurrent manner

limits, achievable accuracy and surface finish The machines used include conventional machines (e.g.,horizontal and vertical milling, drilling, and grinding) and CNC machining centers.

9.2.2 The Process Planning Problem

Given a part and a set of manufacturing resources in a job shop, the process planning problem can begenerally defined as follows:

1 Operations selection — For each machining feature, determine one or several operations required.This includes the selection of machines, tools (cutters), TADs, and fixtures based on the feature’sgeometric and technological specification and available machining resources This process canalso be categorized into two substages: operation type selection and operation method selection

An operation type (OPT) refers to a general machining method without concerning any specificmachines and tools (e.g., end-milling and boring) An operation method (OPM), on the otherhand, refers to the machine and tool to be used to execute an OPT It is a common practice forhuman planners to make decisions over these two stages simultaneously

2 Operations sequencing — Determine the sequence of executing all the OPMs required for thepart so that the precedence relationships among all the operations are maintained Set-ups canthen be generated by clustering the neighboring OPMs that share the same machine and fixturearrangement

It is obvious that decisions made in steps 1 and 2 may contradict each other Therefore, the making tasks in 1 and 2 must be carried out simultaneously in order to achieve an optimal plan For

decision-FIGURE 9.2 Basic machining features.

Through slot

Blind slot Chamfer

each of the decision-making tasks described above, process planning knowledge at both global and locallevels is required, which is described in the following subsections.

9.2.2.1 OPT Selection

OPT selection is carried out by mapping each machining feature to one or a set of OPTs, based onfeature’s type, dimensions, tolerance, and surface finish Feasible solutions can be multiple For example,the OPT for a through hole of 20 mm in diameter without any tolerance and surface finish requirements

is by drilling only, where as the OPT for the same hole but with a surface finish of 3.2 µm can be either(drilling, reaming) or (drilling, boring)

9.2.2.2 Machine (M) and Cutter (T) Selection

For each OPT, a machine and cutter can be selected from the available machined and cutters in the jobshop, based on the geometry and accessibility of the feature to which the OPT is applied Similarly,feasible solutions can be multiple If no such machine or suitable cutter can be found to perform theOPT, it is eliminated For example, an end-milling operation is selected for F1 (pocket) in Figure 9.3 Afeasible end-mill cutter must have sufficient length to reach the bottom of the pocket and its radius is

no more than the corner radius of the pocket

9.2.2.3 TAD Selection

A TAD for an OPM refers to an unobstructed path along which the tool can approach the feature to bemachined Similarly, feasible TADs for an OPT(M/T) can be multiple For a prismatic part, six possibleTADs, i.e., the six normal directions of a prismatic block (±x, ±y, ±z), are assumed For a cutter acting

on a feature alone, its theoretical TADs are fixed However, interference may occur when considering thepart and tool geometry One of the approaches to check such interference is to simulate the movement

of the cutter in a solid mode [Ma, 1999] The solid model of a tool is moved from a predefined pathtoward the feature along its theoretical TADs After reaching the feature, it is moved to cover the entirefeature During this simulation, any TAD that causes interference is discarded If an OPT(M/T) does nothave any feasible TADs, it is discarded Referring to the part shown in Figure 9.3, although drilling athrough-hole has two TADs in theory, a drill can only approach F5 along “+x.”

FIGURE 9.3 A machined block.

F9 (Slot)

F10 (Slot)

9.2.2.4 Fixture Selection

Fixtures are mechanical devices used to secure a workpiece on the machine table during machining Formachining of prismatic parts, modular fixtures and vices are the most common fixture devices [Nee etal., 1995] For an OPT(M/T/TAD), the workpiece geometry is checked to see if a particular fixture can

be applied A vice needs two parallel faces vertical to the machine table while a modular fixture needsthree orthogonal faces based on the 3–2–1 principle Since the workpiece geometry is a result of the OPTsequence, fixture selection can be done during operations sequencing

9.2.2.5 Precedence Relationships between Operations

Although a possible process plan can be a permutation generated from the OPM pool, it is consideredvalid only if none of the precedence relationships (PRs) between operations caused by geometrical andtechnological consideration need is violated In other words, these PRs have to be identified to check if

a randomly generated sequence is valid In general, the PRs between operations can be identified fromthe following:

1 PRs between the realisation of machining features — These can be derived from process straints such as fixture constraint, datum dependency, and good machining practices Some typicalprocess constraints considered are as follows:

con-• Fixture constraint: A PR between two features exists when machining one feature first maycause another to be unfixturable An example is given in Figure 9.3 where F2 must be milledbefore F1, F3, F4, F6, and F7 Otherwise, the supporting area for milling F2 is not sufficient

• Datum dependency: When two features have a dimensional or geometrical tolerance ship, the feature containing the datum should be machined first For example, F4 needs to bemachined before F6 since the side face of F4 is the datum of F6 (see Figure 9.3)

relation-• Parent–child dependency: When a feature (A) must be produced before a tool can access anotherfeature (B), A is called the parent of B For example, F9 can only be machined after F2 isproduced (see Figure 9.3)

• Avoiding cutter damage: For two intersecting features A and B, if machining B after the creation

of A may cause cutter damage, B should be machined first Referring to Figure 9.3, if F8 is ablind hole, it should be drilled before F7 in order to avoid cutter damage However, if F8 is athrough hole, this PR does not hold if “+ z” is chosen as the TAD for drilling F8, provided thatthe slot F2 is not too deep

• Better machining efficiency: For two intersecting features A and B, machining either one firstmay partially create the other If machining A has a larger material removal rate (e.g., an end-mall cutter with a larger diameter) than machining B, A should be machined first Referring to

Figure 9.3, the machining of F4 and F10 is intersected As F10 can only be machined using anend-mill cutter with a much smaller diameter than that for F4, F4 should be machined beforeF10

2 PRs among a set of OPTs for machining a feature — For every set of OPTs obtained throughmapping from a feature, there exists a fixed PR among those OPTs, i.e., roughing operations comebefore finishing operation (e.g., drilling comes before reaming, milling comes before grinding,etc.)

Since the PRs generated based on the above considerations may contradict each other, they can becategorized into two types: the hard PRs and soft PRs The part cannot be manufactured if any of thehard PRs are violated; while the part can still be manufactured even if several softPRs are violated,although the practice is less preferred For example, for the part in Figure 9.3, the PR of F2 → F9 is ahard one, while the PR of F4 → F10 is a soft one By categorizing the PRs into hardand soft ones, afeasible process plan can be achieved when conflicts between the hard PRs and soft PRs arise Generally,

a well-designed part will not present any conflicts between hard PRs

9.2.2.6 Relationships between OPMs and the Sequence of OPMs

Although operation methods selection and operation methods sequencing need to be carried out taneously, in actual operation execution, one is carried out before the other, without considering theresult of the latter To maintain the concurrency between these two activities, the validity of the resultfrom the latter must be checked and ensured to satisfy the result from the former Depending on whichone (OPMs selection or OPMs sequencing) is executed first, the validity of OPMs and a sequence ofOPMs is discussed as follows:

simul-1 Validity of a sequence of OPMs (OPMs selection first) — The validity of a sequence of OPMsrefers to the PRs among the OPMs are satisfied The influence of selecting OPMs first on thevalidity of a sequence of OPMs can be seen from the variation of PRs caused by the selected OPMs

In other words, some PRs are invariant regardless of the OPMs selection, while others are theresults of the OPMs selection Referring to Figure 9.3, if F8 is a blind hole, it should be drilledbefore F7 as discussed previously However, if F8 is a through hole, this PR does not hold if “+z”

is chosen as the TAD for drilling F8, provided that the slot F2 is not too deep This indicates thatsometimes the validity of a PR depends on the selection of TAD or OPMs These kinds of PRsare designated as conditional PRs and the conditions are attached to them When OPMs are selectedfirst, the conditionalPRs can be validated

2 Validity of OPMs (OPMs sequencing first) — The validity of an operation method refers to thefeasibility of applying its M/T/TAD under particular circumstances For instance, anOPT(M/T/TAD) certainly depends on the availability of the M and T, which are naturally ensuredduring the OPMs selection phase When a sequence of OPMs (OPM templates, actually) is selectedfirst, some of the TADs identified earlier for the OPMs may become invalid Referring to Figure9.3, F8 (through-hole) has two alternative TADs, i.e., “+z” and “–z.” If OPM(F7) precedes OPM(F8)

in a preselected sequence, OPM(F8) with “–z” will be invalid This indicates that sometimes thevalidity of a TAD or OPM depends on the sequence of OPMs These kinds of TADs are designated

as conditional TADs and the conditions are attached to them When a sequence of OPMs is selectedfirst, the conditionalTADs can be validated, as well as the OPMs they belong to

9.2.2.7 Grouping Effect on the Validity of OPMs

In addition, there are situations where a group of OPMs are needed for producing a feature In that case,the validity of an OPM may depend on its neighbouring OPM For instance, a through hole can beproduced through (centre-drilling, drilling, reaming) from a solid part Although drilling may have twoopposite TADs in theory, in the OPM selection for drilling, it must have the same TAD as centre-drilling

in the same group

9.2.3 The Process Plan Evaluation Criterion

In the last section, the solution space for a process planning problem is described based on discussions

on various selection stages and constraints It is obvious that there can be many feasible solutions for agiven process planning problem Therefore, there is a need to find the best plan In order to achieve this,

an evaluation criterion must be defined

Currently, the most commonly used criteria for evaluating process plans include shortest processingtime and minimum processing cost Since the detailed information on tool paths and machining param-eters is not available at the stage where only operation methods and their sequences are determined, thetotal processing time cannot be accurately calculated for plan evaluation On the other hand, the pro-cessing cost can be approximated Generally, the processing cost for fabricating a part consists of twoparts, i.e., cost due to the usage of machines and tools and cost due to set-ups (machine change, set-upchange on the same machine, and tool change) For the cost due to machine usage, a fixed cost is assumedevery time a particular machine or a tool is used For the cost due to set-ups, a fixed cost is assumedwhen a particular set-up is required Based on these assumptions, five cost factors (machine usage, toolusage, machine change, set-up change, and tool change) for a process plan can be derived as follows:

1 Machine usage cost (MC)

where TCI iis the tool cost index for using tool i, a constant for a particular tool

3 Machine change cost (MCC): a machine change is needed when two adjacent operations areperformed on different machines

Equation (9.4)

where SCCI is the set-up change cost index, a constant

5 Tool change cost (TCC): a tool change is needed when two adjacent OPMs performed on the samemachine use different tools

Equation (9.5)

where TCCI is the tool change cost index, a constant

The sum of these five cost factors approximates the total processing cost On the other hand, thesecost factors can also be used either individually or collectively as a cost compound based on the require-ment and the data availability of the job shop For example, if a process plan with minimum number ofset-up changes is required, the evaluation criterion is set as SCC This criterion setting provides muchflexibility required by different job shops

MC MCI i i

9.2.4 An Optimization Process Planning Model

Based on the above discussion, the solution space for a process planning problem can be explicitly formed

by all the OPTs required, the available OPMs for each OPT, possible sequences, and various constraints

A modelling algorithm to obtain this solution space is described as follows:

Algorithm: process planning modelling

Input: A set of features, job shop information (machines and tools)

Output: Number of OPTs required, OPMs (M/T/TAD) for each OPT, precedence relationships

between OPTs

1 For each feature, find all the OPTs that can achieve its attributes (shape, dimensions, tolerances,and surface finish), based on shop level machining capabilities The resulting OPTs for a featurecan be expressed as one or several sets of (OPT1, OPT2, , OPTk), where k is the total number

of OPTs required in a set It is worth mentioning that different sets of OPTs for a feature mayhave different number of OPTs For example, a blind hole can have two sets of OPTs: (centre-drill, drill, ream) and (centre-drill, mill) To achieve a uniform representation, the concept of a

“dummy” OPT (D-OPT) is introduced, which is defined as an operation incurring no machiningcost and having no effects in terms of machine, set-up, and tool changes By adding the dummyoperation into the sets that have fewer operations, each set will have the same number of OPTs.For the above example, the two sets of OPTs for producing a hole can be expressed as (centre-drill, drill, ream) and (centre-drill, D-OPT, mill) Finally, the total number of OPTs required forthe part is determined

2 Identify all the PRs among features as well as among the OPTs in each set for a feature Convertthe PRs between two features to the PRs between their OPTs according to the following rule:

IF F(X) → F(Y)

AND OPT(i) ∈ F(X)

AND OPT(j) ∈F(Y)

THE OPT(i) →OPT(j)

As a result, both hard and soft PRs are identified The hard and softPRs are compared pair-wise

If a conflict arises, the respective soft PR in the respective conflict is deleted

3 For each OPT, find all the combinations of machines (Ms) and tools (Ts) with which it can beexecuted, based on machine-level machine capabilities

4 For each combination of M and T, find all the feasible TADs

5 Attach a condition to a TAD if it depends on a particular sequence or the assignment of a TAD

to a particular OPT Similarly, attach a condition to a PR if it depends on the assignment of aTAD to a particular OPT

End of algorithm process planning modelling

The output from the above algorithm is a fixed number of OPTs required for fabricating a part andalong with each OPT, one or several sets of (M/T/TAD) The PRs among the OPTs are also explicitlyrepresented To further illustrate this novel process planning modelling technique, the part shown in

Figure 9.4 is used as an example It is constructed by adding several machining features to a chuck–jaw

in order to pose certain difficulty for process planning The dimensions and tolerances are also shown

in Figure 9.4 The machining process starts from a rectangular block It is assumed that the block is machined to a size of 160 × 50 × 70 Therefore, the part consists of 14 features as shown in the figure.This part is assumed to be machined in a job shop, in which the available machines are: one three-axisconventional vertical milling machine (M1, MCI = 35), one three-axis CNC vertical milling machine(M2, MCI = 70), one drill press (M3, MCI = 10), one conventional horizontal milling machine (M4,

pre-MCI = 50), and one CNC horizontal milling machine (M5, MCI = 85) It is assumed that all the machines

are capable of accommodating the part The available cutting tools with their dimensions as well as their

cost indices are shown in Table 9.1 The other cost indices are assumed to be MCCI = 110, SCCI = 90,

and TCCI = 20 The process planning modelling process for this part is described as follows:

1 OPTs — Based on the shop floor machining capability, the identified OPTs are shown in column

3 in Table 9.2 It can be seen that only F5 and F6 need three OPTs (centre-drilling, drilling, and

end-milling) each, while the remaining features need one OPT each (the two holes, F11 and F12,

do not need centre-drilling, as there is no special tolerance requirement) The total number of

OPTs required to fabricate this part is 18

FIGURE 9.4 An example part and specifications.

2 PRs — Firstly, the PRs between features are identified F1 should be produced after F2 and F8 for

better machining efficiency F4 should be produced after F3 due to parent–child relationship F5

should be produced after F3, otherwise the drill (T14) would not be able to reach the bottom of

F5 On the other hand, F5 should also be produced after F1 as F1 is the datum feature of F5 F7

should be produced after F5 to avoid cutter damage F7 should also be produced after F1 to ensure

sufficient supporting area for machining F1 F11 and F12 should be drilled before F10 to avoid

cutter damage F14 should be machined after F9 due to parent–child relationship Similarly, F13

should be machined after F9 These PRs between features are then converted to PRs between their

respective OPTs Secondly, the PRs between OPTs for every individual feature are identified, where

only features needing multiple OPTs are concerned As a result, OPT5 should proceed OPT6 and

OPT6 proceed OPT7 for F5; OPT8 proceed OPT9 and OPT9 proceed OPT10 for F6 Finally, these

two sets of PRs between OPTs are merged, as shown in Table 9.3

TABLE 9.1 Cutting Tools and Their Cost Indices (TCI) Used in the Job Shop

a For T-slot_cutter, the second parameter in the bracket represents the width of the cutter.

TABLE 9.2 OPTs and Possible OPMs (M/T/TAD) for the Part in Figure 9.4

M4, M5

T1, T2, T3, T4 T5

M1, M2, M3, M4, M5 M1, M2, M3, M4, M5 M1, M2

T10 T14 T3

M1, M2, M3, M4, M5 M1, M2, M3, M4, M5 M1, M2

T14 T14 T3

+x, –z +x, –z

3 Ms and Ts — The suitable machines and tools are selected to perform each OPT based on the

consideration of the shape-producing capability of the machine, tool dimension, and the respectivefeature geometry The results are shown in column 4 and column 5 of Table 9.2 If an OPT hasmore than one row of Ms and Ts in the table, the possible combinations of Ms and Ts are derivedamong the alternatives in the same row For example, the possible combinations (M/T) for OPT18are M1/T11, M2/T11, M4/T18, and M5/T18

4 TADs — The suitable TADs for each OPT (M/T) are identified as shown in column 6 of Table9.2 The representation of TADs is based on the coordinate system (x–y–z) shown in Figure 9.4.Similarly, the possible combinations (M/T/TAD) for every OPT are derived among the alternatives

in the same row

5 Conditional TADs and PRs — For this example, there are no conditional TADs or conditional PRs.

Up to this point, an explicit solution space for the example is successfully developed The next step is

to choose an objective function (cost compound) and “to identify a combination of (M, T, TAD) forevery OPT and put them into an order that does not violate any precedence relationships between anytwo OPTs while achieving the least cost compound.” Based on this definition, there could be more thanone optimal solution for a given process planning problem, and each has the same cost compound butdifferent operation methods and/or sequence order

For a part needing n OPTs and if OPT-i has m(i) alternatives, the total number of feasible process plans (K) for the part is expressed as

Equation (9.6)

where is the number of invalid sequences Obviously, to find the best plan by enumerating all thefeasible process plans is a NP-hard problem Therefore, optimization search algorithms should be used

to find an optimal or near-optimal solution

9.2.5 Set-Up Plan Generation

Upon solving the process planning model using a search method, the output is a set of OPMs (M/T/TAD)

in a sequence A set-up plan can then be generated by grouping the neighboring OPMs, which share thesame machine and TAD, into the same set-up The details are given as follows:

Algorithm: set-up plan generation

Input: All the OPMs (M/T/TAD) in a sequence

Output: Set-ups in sequence; each includes a set of OPMs in sequence

1 Start with the first set-up with the first OPM in the total OPMs sequence as its first OPM This

set-up is called the current set-up and the first OPM the current OPM.

TABLE 9.3 Precedence Relationships between OPTs for the Part in Figure 9.4