Artificial intelligencefor production planningby Frank Mill and Stuart SpraggettCoventry (Lanchester) Polytechnic

Artificial intelligence tools have recently become a significant topic inmany areas of engineering research. The use of computer-baseddecision support systems is potentially of great benefit in thecomplex task of process planning, both in terms of consistency ofapproach and in the reduction of manufacturing lead times. Thispaper discusses a problem-solving system which carries out processplanning tasks for a small flexible manufacturing system.

Introduction

In recent years a great amount of efforthas been spent attempting to automatevarious activities in the manufacture ofsmall batches of mechanical com-ponents. Computer-aided design tech-nologies such as automated drafting,solids modelling and finite-elementanalysis are making a major impact onthe design departments of many firms,while computer-aided manufacturingadvances in computer numerical con-trol, direct numerical control, robotics,intelligent conveying, automatic inspec-tion and the integration of these intocomplex control structures are chang-ing the way small batches of com-ponents are made.

Between the design and make activ-ities, however, lies the task of pro-duction planning which involves theinterpretation of designs and the sub-sequent production of a set of manu-facturing plans. Part of this probleminvolves efficiently scheduling the flowof work through the manufacturingsystem. It is in this area that substantialimprovements have been made inrecent times; these are a result of bothmathematical advances and the exis-tence of powerful computational andsimulation tools. The process planningactivity, on the other hand, has gainedmuch less from the availability of newtechnologies. The production of timeand cost estimates for components isoften aided by the use of computers,but attempts to produce systems whichcan make decisions about machine sel-ection or the sequencing of cutting op-erations have resulted in only partialsuccess, and these are usually specificto given application areas.

At the present time there is consider-

able interest in the application of artifi-cial intelligence (Al) techniques inengineering. These techniques may beemployed to produce diagnostic facili-ties on machine tools during manufac-ture, for example, while Simmons [1]has pointed out the possible benefitswhich might be realised if Al can beapplied in computer-aided engineeringdesign. In production planning too Altechnology appears to be capable ofmaking major improvements on existingsystems.

The process planner's job involvesreasoning about and interpreting engin-eering drawings. He or she has to makedecisions on how cuts should be madeand in what order the cuts should beexecuted, as well as deciding which ma-chines and tools should be used andwhat fixturing is necessary for holdingthe component during cutting.

These decisions are often made as aresult of subjective judgments on thepart of the process planner involvedand are prone to inconsistency. Notonly are plans for identical parts likelyto vary between different process plan-ners, but they will also vary over time inthe case of a single process planner.The type of plans which a planner pro-duces depend on the individual's tech-nical ability, the nature of his or herpast experience and even on theperson's mood at the time of planning.

Computers may be used to performwhat would otherwise be time-consuming calculations, but they havehad little impact on the decision-making functions which are involved inprocess planning work. It may also beargued that the decision-making pro-cess itself has become more complexfor many planners, given the fact thatmanufacturing technologies change

constantly and new options in tools andmaterials are continually being madeavailable.

The process planner must possess ahigh degree of manufacturing knowl-edge to accomplish his or her task in asatisfactory manner. Thus the planner isoften expected to have considerablepractical experience as well as a highstandard of formal education. Suchpeople are likely to be difficult andexpensive to recruit and hold for manyfirms.

Another problem which is oftenassociated with process planning is thesignificant contribution it makes tomanufacturing lead times owing to thetime it takes to manually develop plans.The use of computer-based decisionsupport systems in process planningcould greatly reduce many of the prob-lems discussed, while the technologyneeded to produce such systems maynow be emerging.

Computer-aided process planning

The idea of using computers to helpproduce process plans was first dis-cussed by Niebel [2] in 1965. Sincethen many efforts have been made todevelop process planning systems andthese have fallen into two distincttypes, known as variant and generativesystems.

The variant approach involves storingand retrieving standard sets of plans forparts which are usually classified intofamily groupings on the basis of theirgeometric shape. These systems areuseful in situations which allow for aneat and simple coding of all the partswhich are handled. The data manage-ment facilities made possible by thecomputer can greatly reduce the timeand cost involved in producing manu-facturing plans, as well as helping toensure that the plans will be producedin a more consistent way.

As Steudel [3] has pointed out, how-ever, the standard plans must be codedby an experienced process planner andwill probably be used and maintainedby him or her. Not only is this costly

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but the computer is only a tool in amanual process planning activity.

The generative method of processplanning, on the other hand, involvesthe automatic generation of a uniqueplan for each component and so doesnot require the storage of standard rou-tines. Instead tentative plans are gener-ated and tested for suitability, and thenthe best alternative is chosen accordingto the system's optimisation criteria.Generative systems are usually depen-dent on the use of a very detailed geo-metric coding system for components,and as a result use more resources atthe coding stage than variant systems.

Recent years have seen the develop-ment of several generative systemswhich have been created for specificapplication domains. Trusky [4] de-scribes a system for a gear cutting firm,while Wysk [5] has developed theAPPAS system for use in milling andhole creation applications.

Halevi [6] outlines a system whichwas designed to produce plans forcylindrical components and also pointsout the size of the sequencing and ma-chine selection problem. For example,consider a component consisting of Nindependent and different features suchas holes. These may be cut in anyorder and each may be produced onany of M machines. The total numberof different possibilities P for manufac-turing the component may then begiven by:

p = N\ x MN (1)

Thus, for a simple part with ten holeswhich is to be made in a workshop withten candidate hole creating machines,there are 3.6 x 1016 alternatives. Even ifeach alternative could be evaluated inone millionth of a second it would takeapproximately 1000 years to computeall the alternatives so that the best plancould be picked out. In practice, how-ever, it is possible to consider only atiny fraction of the combinations bycutting out seemingly fruitless pos-sibilities using simple rules of thumb. Inthe past, when researchers have devel-oped generative process planning sys-tems they have tried to write programswhich find 'optimum' plans, althoughthis approach has several drawbacks.

The calculations involved in theevaluation of cutting processes areoften misleading because these are sub-ject to substantial errors caused bylarge tolerances on the variables used inthe analytical equations which are typi-cal of process planning. For example,the equations or tables which are usedto calculate the metal cutting condi-tions required for a suitable surfacefinish often use variables which havetolerances of plus or minus 50%.

But even if the production of an opti-mal process plan could be guaranteedthere are still questions about the desir-ability of a single rigid optimal plan.Such a plan may be costly to find interms of time and might not fit in wellwith the subsequent production sched-ule. Optimal plans may contribute littleto a firm's profitability if a componenthas to wait hours or even days to getonto the machines which are requiredto carry out its optimum cuttingsequence.

The requirements which may beplaced on future process planning sys-tems may be different from those of thepast as computer-aided manufacturingtechnologies make for advanced pro-duction methods such as those em-bodied in a flexible manufacturingsystem (HMS).

One of the major benefits which canbe realised by a successful FMS install-ation is the avoidance of excess work inprogress and the correspondingly fastthroughput times. In the manufactureof small batches of components thiscan only be gained if the work can bescheduled to flow efficiently throughthe manufacturing system. Thus thedemands placed on the process plan-ning activity by an FMS may have dif-ferent priorities than those previouslyassociated with process planning.

A process planner for an FMS shouldideally take on a, much more integratedrole than might be expected in tradi-tional manufacturing systems. Such aplanner would operate on a more sys-temic concept of efficiency and mightallow the manufacturing system to op-erate closer to its optimum while stillproducing efficient cutting plans(although these might be semi-optimalrather than true optimal plans). Thiswould require that the process planneractively contribute to system efficiencyby including in its decision-making logicsome consideration of parameters usedto measure the status of the manufac-turing system (for example machine ortool availability). Alternatively, the plan-ner might make a passive contributionby outputting alternatives to its firstchoice plan. This may allow more flex-ibility in planning the schedule for aproduction period or may allow a real-time scheduling system to choose alter-natives in the result of breakdowns.

Artificial intelligence inproduction planning

A possible method of improving existingprocess planning and scheduling activ-ities lies in the technique of enhancingpresent methods with the addition ofintelligent knowledge-based systems.Considerable interest in the use of these

and other artificial intelligence tools hasrecently come about, and currentlymuch research is being geared towardsthe use of intelligent knowledge-basedand expert systems in engineering appli-cations.

One job shop scheduling system hasbeen described by Fox e( a/. [7], whilearticles discussing the possibilities of theuse of artificial intelligence techniquesin process planning work have recentlyappeared. Nau and Chang [8] have dis-cussed the application of expert sys-tems techniques using hole creation asan example. Hannam and Plummer [9]describe the successful work carried outby them in attempting to build 'goodproduction engineering practice' into aprocess system for turned parts.

An expert system for process plan-ning called GARi is the result of re-search described by Descotte andLatombe [10]. This system consists of aplanner and a 'knowledge box'. The'box' contains the manufacturing ruleswhich are used to guide the search fora solution to the process planning prob-lem for a given component.

Research carried out by the authorshas resulted in a problem-solvingsystem which carries out process plan-ning tasks for a small flexible manufac-turing system. Like other artificialintelligence problem solvers the modelis made of three components: a data-base, a set of production rules, and acontrol strategy. The database holds de-tails of part geometry, blank-materialgeometry and the machines available,as well as storing details of the cuts tobe taken.

The production rules consist of aseries of operators which perform trans-formations on the database in order toachieve specified goals. Such rules arestatements of knowledge about theproblem domain and how parts of aproblem may be solved. The decisionson which of these rules to apply and inwhich order are a matter for the pro-cedural semantics of the system andit is the operation of these whichimplements the control strategy of aprogram.

The model starts by using the partdescription as a definition of a problemand regards the description of the blankmaterial as a goal state. Thus thesystem attempts to transform the partdescription into the blank descriptionwhile the details output by the trans-formation are a series of metal additionsto the component. These are used torepresent a reversed and tentativelysequenced list of cutting operations.This list is then put into a final sug-gested sequence of operations and thebest machines to carry out the oper-ations are also assigned, on the basis of

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plane([p11,yn,0,100,0,0,0,40])plane([p12,zp,0,100,ai50,40,4achole,h1,sf10])plane([p13,yn,0,100,150,150,40,80])plane([p14,zp,0,100,150,200,80,80])plane([p15,xp,100,100,0,200,0,80])plane([p16,yp,0,100,200,200,0,80])plane([p17,zn,0,100,0,200,0,0,chole,h1])plane([p18,xn,0,0,0,200,0,80])hole([h1,z,0,20,50,75,pplane,p12,p17,dtpm10])

Fig. 1 Component and its part description

a complex cost system. Alternativeplans and options also result from theplanner.

In the model, both the part to beproduced and the blank material fromwhich it is to be cut are represented bythe same descriptive technique. Thisallows any shape of blank material tobe specified to the system. Such blanksmay include partially completed com-ponents which are a result of a castingor other process for example.

The description technique used isbased on the general geometry of thecomponent and infers little about themanufacture of the component. Inusing the technique the part is de-scribed as a list of features, such aspockets, slots, planes or holes for exam-ple. The description of a feature itselfconsists of a list of attributes as shownin Fig. 1. The list contains such informa-tion as feature name, parent features'names, child features' names, toler-ances, the names of features to whichthese tolerances refer and a geometricaldescription of the feature itself.

The description only gives rough geo-metrical information; in the case of aplane, for example, only the minimumand maximum distances from the axesare given. This is sufficient for planningpurposes since details of the exactshape are not necessary until part pro-gramming is being undertaken, whileany difficult areas requiring special at-tention are referred to as separate childfeatures.

The description of machines in thesystem is done in a similar way to thepart description, whereby the machineis represented by a capability profile

which gives details of the shape cre-ation facilities available. As in partdescription a list is included detailingmaximum sizes, tolerances achievableand the cost of using the machine.

The planning model is composed of anumber of modules, each of which hasits own set of heuristic information. Thisis stored in the form of simple rulesabout how to perform a given taskunder set conditions. These rules maybe regarded as being in the format ofIF-THEN type statements. For example,the first step the model takes is to givea tentative order to the features on acomponent. This is done purely byapplying a rule to the componentdescription. These rules may be ex-pressed in a verbal form:

IF the feature being considered is ref-erenced to in tolerances

THEN accept the feature onto thenew feature list

ELSE consider another feature.

After the execution of such a ruleanother may be applied to order theremaining features. It is these ruleswhich collectively make up the knowl-edge bases in the model.

Although the rules described abovemake up the knowledge of the model,their efficient implementation dependson the control strategy of the program.The way in which rules are applied de-pends on the order in which they areplaced and on the operation of specialcontrol rules which transfer controlfrom one part of a program to another.It is this control technique which imple-ments the search strategy for a solutionto a problem. Many different types of

search strategy have been developed inorder to search efficiently for solutionsto problems and these vary enormouslyin both their scope of selection (theextent to which alternatives areconsidered) and their scope of recovery(the extent to which they can reversepreviously made decisions).

In attempting to solve a number ofcomplex problems such as thoseencountered in the process planningactivity, it is unreasonable to try to usea single solution search strategy, and soa number of different types may becombined depending on the sub-problem at hand and a unique hybridstrategy may be formed.

This is the approach which has beenadopted in the building of the processplanning model, and indeed it is theability to experiment with different stra-tegies which is the reason for themodel's development.

After initially sorting the features of apart into a tentative order, the modelcan then choose a selection of suitablemachines for each cut to be taken. Theinitial choice for a given feature is donein the following way. The model checksthe parameters given in the machinecapability profile and discards machineswhich cannot perform the given task;then the choice is made on a cost basis,the cheapest M machines being sug-gested and filed with the cut details. Ifonly the cheapest three machines areconsidered, this has the effect of settingM = 3 in Eqn. 1, giving:

P = N\ x 3N (2)

Thus the search space has been re-duced by removing the need to searchseemingly expensive possibilities.

When this selection procedure iscomplete it is then possible to makedecisions on the sequencing of oper-ations and on the actual machine to beused for each operation. The searchspace for this subproblem may be im-mense, and a great deal of computationmay still be required if an optimum sol-ution to this subproblem is to be found,and at this stage heuristic information isused to narrow down the possibilities. If,for example, a single anteriority rule canbe applied such that a certain cut Amust be performed before a cut B, thenthis could reduce the number of com-binations in Eqn. 2 to:

P = (N!/2) x 3N

Any further anteriority rules which canbe applied would further reduce thesize of the search space.

Rules are subsequently used to groupoperations into set-ups and a simple al-though crude method of detectinggroups is applied. This uses the assump-tion that any cuts being accessed in the

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same direction may be performed inthe same set-up. Next, the model findssubgroups within set-up groups, whichare operations which can be done onthe same machine and in the same set-up. The model can now detect thenumber of machine-to-machine move-ments and set-ups required and assignscosts for these in the plan.

The final step in this stage of selec-tion consists of considering alternatives.From the list of alternative second- andthird-choice machines the model triesto substitute machines into wholephases of operations and tests to see ifthe increase in machine cost can bemore than compensated for by a pos-sible decrease in the number ofmachine-to-machine movements re-quired. Following this the choice of ma-chines is set, as is the sequence ofphases.

Final tool selection is not carried outby the model for two reasons. First, itwas considered that the task of tool sel-ection may be done in a way similar tothat of machine selection, and secondlythe argument that tool selection shouldbe done as late as possible in an FMS isaccepted. This allows decision makingabout tools to be carried out withregard to the production schedule andthe tool sets to be used. Calculations onspeeds and feeds of the cutting processcan be done after tool selection, poss-ibly using a computer-based machine-ability data system.

Costs

As mentioned earlier, manual methodsof process planning often make use ofthe planner's general knowledge of thecurrent and past production schedules,and possibly even of future ones. Com-ponents can be directed to machineswhich are not usually too busy, thuseasing or smoothing the productionschedule. In the event of things goingwrong the workshop foreman may alsochange machines or alter plans. In themodel described, machines andsequencing were selected on cost cri-teria. The costs used in the model arenot set in a conventional manner, how-ever. In addition to machine costs perhour, additional weighted costs areadded or subtracted from a feedback inthe production schedule.

This might be used in a number ofways. It is possible to adjust the cost ofa machine depending on its state ofloading. When a nominal productionschedule is set it is possible, also, torerun a process plan for one or morecomponents and make an overloadedmachine more expensive so as toreduce the process planner's propensityto use that machine. If one wished to

exclude a machine owing to break-down, for example, then that machine'scost can be set to infinity, thus effec-tively excluding it from process plan-ning consideration.

The model also generates alternativesfor each operation and phase if theseare possible, and this might allow ascheduler some flexibility. In real-timecontrol of the manufacturing facilitiesthe use of alternative machines mayallow a component to be redirected toother machines in the event of break-downs.

The future

The model which is described in thispaper was originally intended as an ex-periment to see if artificial intelligencetechniques might prove useful inautomating some process planningtasks and also to help the researchersdevelop an understanding of what heu-ristic techniques might be used. To thisend the model has been very successfuland it is intended to develop the modelmore fully. There is much work whichcould be done in this area.

While the model was being devel-oped the part description formatevolved as a result. Some work has nowbeen done on considering a formal ap-proach to coding parts using a hier-archy of features. However, greatbenefit might be gained if codes forprocess planning could be generatedautomatically by an interpreter inter-faced to an engineering database.

The model could also be improvedby providing a larger number of fea-tures, as well as significantly more com-plex ones such as planes which do not

lie parallel to any axis, or possibly sculp-tured surfaces.

The model uses a simplistic methodfor reasoning about set-ups, and itwould be useful to investigate thedesign of a more realistic and corre-spondingly more sophisticated tech-nique. Although outside the scope ofthe present work, it would be worthwhile to investigate the possibilities offixture selection and positioning usingartificial intelligence techniques.

It will take some time before the pro-cess planning activity can be automa-ted; however, the techniques whichmight be used to achieve this goal maynow be beginning to emerge.

Implementation

The model which has been described inthis article was written in C-Prolog run-ning under the UNIXf operating system.The scheduling work is manually pre-pared and is verified on a simulation ofthe FMS on an Istel SEE WHY system.Tool paths are generated and tested ona Micro Aided Engineering NC machinetool simulation system.

Acknowledgments

The authors would like to give sincerethanks to the following persons: atCoventry (Lanchester) Polytechnic, JimDavis of the Department of ProductionEngineering, and Alan Chantler, NickGodwin, Simon Ritchie and Rob Lucasof the Department of Computer Sci-ence; and Nigel Kay of the National En-gineering Laboratory.

f UNIX is a trademark of Bell Laboratories.

References

1 SIMMONS, M. K.: 'Artificial intelligence for engineering design', Computer-Aided Engin-eering journal, 1984, 1, (3), pp. 75-83

2 NIEBEL, B. W.: 'Mechanised process selection for planning new designs'. AmericanSociety of Mechanical Engineers, 1965, Paper 737

3 STEUDEL, H. J.: 'Computer-aided process planning: past, present and future', Internation-al journal ol Production Research, 1984, 22, pp. 253-266

4 TRUSKY, H. T.: 'Automated planning reduces costs', American Machinist, 1984, April, pp.80-82

5 WYSK, R. A.: 'An automated process planning and selection program: APPAS'. Ph.D.Thesis, Purdue University, West Lafayette, IN, USA, 1977

6 HALEVI, C.: The role of computers in manufacturing processes' (J. Wiley, 1980)7 FOX, M. S., ALLEN, B. P., SMITH, S. F., and STROHM, C. A.: 'ISIS — A constraint-directed

reasoning approach to job shop scheduling: system summary'. Report CMU-RI-TR-83-8,Carnegie-Mellon University, Pittsburgh, PA, USA, 1983

8 NAU, D. S., and CHANG, T. C.: 'Prospects for process selection using artificial intelli-gence', Computers in Industry, 1983, 4, pp. 253-263

9 HANNAM, R. C, and PLUMMER, J. C. S.: 'Capturing production engineering practicewithin a CADCAM system', International journal ol Production Research, 1984, 22, pp.267-280

10 DESCOTTE, Y., and LATOMBE, J. C : 'CARi: a problem solver that plans how to machinemechanical parts'. Seventh International Joint Convention on Artificial Intelligence, Van-couver, Canada, Aug. 1981

F. Mill and Dr. S. Spraggett are with the Department of Production Engineering, Coventry(Lanchester) Polytechnic, Priory Street, Coventry, Warks. CV1 5FB, England

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