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PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS V1JAY A. TIPNIS ABSTRACT Unit manufacturing operations are at the heart of every manufactur- ing system. Process and economic models are essential tools in designing, developing, planning, optimizing, and controlling manufacturing operations and systems. The status of the development and application of these models is presented along with the real- world problems and challenges that influence their use. INTRODUCTION At the heart of every discrete parts man- ufacturing system, whether traditional or flexible, attended or unattended, are man- ufacturing processes that convert input ma- terial into a prescribed part or assembly configuration. The central purpose of every manufacturing system is to achieve the transformation at the most desired produc- tion rate and cost. All other operations, including data flow, material handling, set- ups, loading and unloading operations, in- spection and quality control, preprocessing, resource supply, and support systems such as tooling, maintenance, and cleanup, must be considered to be in support of the trans- formation of a starting material into a final product. Most of the improvements that have re- sulted from automated manufacturing sys- tems can be identified with a drastic reduc- 92 tion in the time and cost associated with these nonprocessing support operations. The challenge for future automated systems is to continue to accomplish a reduction in these nonprocessing operations while also en- couraging unattended operation for a pre- determined period of time. Achievement of this will require a sufficient understanding of the processes to allow the construction of reliable models for designing, planning, op- timizing, and controlling unit manufactur- ~ng processes. Modern manufacturing system design is still evolving into a cohesive methodology where diverse technologies of design, mate- rial science, material processing, numerical control, quality control, material handling, sensors, computer networks, computer soft- ware, data-base systems, and man-machine interaction must be integrated. The role of processing is crucial in accomplishing this. Economic models, as complements to
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS i, process models, have evolved for unit man- ufacturing processes, sequences of manu- facturing processes, and total manufactur- ing systems. Although progress to date shows promising applications, further re- search is needed to accommodate various materials, process types, and complete man- ufacturing systems in these models. Fur- thermore, the economics of product and process design and development must be better understood to ensure that the designs are producible at the desired cost level. Some of the crucial gaps that still exist n the design-manufacturing interface and some of the deficiencies in the state-of-the- art process and economic models are out- lined in this paper. In addition, an attempt is made to relate the actual manufacturing process to the manufacturing system de- sign. ACHIEVING THE DESIGN INTENT The design intent can be achieved in a variety of ways. Material type, part fea- tures, tolerances, finishes, and fit require- ments can often be modified without jeop- ardizing the part, assembly, or component function. Such modifications in design can ensure that a cost-effective manufacturing process will be used. This has been demon- strated for discrete parts used in the aero- space, automotive, and precision parts in- dustries, where a large fraction of the costs can be influenced through an effective de- sign-manufacturing interface. Although a formal organization dealing with the design-manufacturing interface does not exist in most corporations, it is not uncommon for an ad hoc personal relation- ship to exist between the design and ad- vanced manufacturing groups. While this arrangement can deal with important as- pects of an issue, the focus is often limited to specific product items. A stronger inter- action and a formal communication link be- tween design and manufacturing are clearly needed and can contribute to achieving a 93 design based on the requirements for assem- bly, service, and maintenance throughout the life of the product. Furthermore, this should ensure that the design will be ration- alized for the capabilities of the manufac- turing system that is going to convert the design intent into reality. To accomplish this will require that some significant unre- solved issues be addressed. Several of these are discussed in the following paragraphs. Subsequent sections discuss a number of them more extensively. Representation of the Physical Object It is well known that engineering draw- ings (views or isometrics) do not guarantee that the object represented is physically re- alizable. Imaginary objects can be repre- sented on paper or on a computer graphic system. Although computer graphics has progressed through several stages of repre- sentations, including wire frames, polygon schemes, sculptured surfaces, and solid modeling, there are no intrinsic criteria to assure that a drawing represents a physi- cally realizable object. The problem of guaranteeing a physi- cally feasible object requires (a) a valida- tion for checking internal consistency of the physical features of the object, (b) a crite- rion for ensuring against under- or over- dimensioning, and (c) a consistency and ad- equacy test for tolerances. While currently available solid modeling systems address some of these problems, more work is needed to establish validation criteria based on the topology of the objects. Over- and under- dimensioning have been the subject of re- search for the past decade (Hillyard and Braid, 1977; Requicha, 1977; Light, 1979~. While constraints on geometry can indicate whether the drawing is under- or over- dimensioned, there are no available criteria to determine which of the dimensions are under or over. In spite of these deficiencies, interactive conceptual design and drawing systems using computers have been com-
94 mercialized by Metagraphics Company and by Cognition Company. Until these limita- tions are eliminated, the manufacturing en- gineer must continue to decide if the repre- sentation is physically realizable, whether it is over- or under-dimensioned, and whether the specified tolerances are consistent. Toleranc~ng of Me Drawing Tolerancing is a convention that started during the late 1920s and had matured to the ANSI Y 14.5 standard by 1973 (Voelcker, 1988, in this volume). No existing mathe- matical theory ensures uniqueness, consis- tency, or completeness for the tolerances of a drawing. Current computer graphic sys- tems, whether wire frames, bounded sur- faces, sculptured surfaces, or solid models, present nominal dimensions. Tolerances are merely attached as labels. Although ICES/ PDES committees are working to develop such capabilities, most computer data bases cannot currently capture tolerances. A draft of PDES (Smith, 1987) gives representation formats for geometry and tolerancing. Sur- face finish and surface integrity remain to be addressed. Furthermore, a toleranced drawing does not represent a unique physical object. Since different manufacturing processes can generate objects with distinct tol'?r~nr bands and distributions (Bjorke, 1978), it is not uncommon to find that parts manufac- tured to tolerance limits may not be capa- ble of assembly (Whitney et al., 1988, in this volume). Selective assembly, part mat- ing, and tolerance stacking are often used as a means of compensating for these in- adequacies. Process Determination From a dimensioned and toleranced drawing, and specifications for the material and a determination of the application con- straints, the next step is to derive a complete set of process sequences for production of VIJAYA. TIPNIS the physical object. Not only are there no criteria and methodologies to determine these steps automatically, the procedures used by highly skilled and experienced man- ufacturing engineers do not yield unique answers. Manufacturing engineers com- monly begin by comparing the part size, shape, features, material, and tolerances against the process capabilities. They then proceed (a) from the goal of the specified finished object to intermediate steps by adding the necessary "stock allowance" at each preceding processing step or (b) from a target blank to the specified finished ob- ject by subtracting the allowance or (c) by applying both (a) and (b) schemes alter- nately. The logical representation of the se- lection procedure cannot be readily char- acterized. It is clear, however, that each process is capable of generating a specific tolerance distribution on a given material, and hence, regardless of the designer's specification, the selected process and material combination truly determines the tolerances on the man- ufactured object. Trade-Offs Among Features, Tolerances, Quality, and Cost Achieving an optimal design requires careful consideration of all aspects of the product and the manufacturing system. The ad hoc interaction between design en- gineering and manufacturing engineering frequently occurs as shown in Figure 1 (Tipnis et al., 1978~. The part design con- cept goes through a series of iterations in which design considerations of function are weighed against manufacturing considera- tions of productivity and cost within the context of the prescribed quality levels. The manufacturing engineer determines the pos- sible trade-offs between cost and such at- tributes as features, tolerances, and quality. The challenge is to formalize the interac- tion so as to ensure that a complete set of trade-offs has been derived.
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS D - lgn - Manutacturlng Interface Part \ | Design d~lgn ~ I , conalder- ooncopt / ~ aeons , . 1 Esffma~on 1 Desl~n - co~Wme I data bad ~ ~ l~- _ _ meets r . l Prellmina~y Mfg. ! ~lon cagnanl~r- 1- ,- ~ ~ r Proc~Plannlng Prell ;~;~\ QC for pan vL farm-out Dane / I paw ~ ~ 1 . Final part \_L drawing / I 1 . L _ _ ~ - Preplanning macroeconomic I analysis ~ Detall planning AT macroeconomic analysis Detalled QC and Inspection plans 1 Macro- micro dam base l ! . _ _ _ _ Schedule and produce - ~ . r Quality levels reports , _ ~ ~~ | Part code | - Part family L Part~c~um In~cUon reports data ban l FIGURE 1 Representation of design-manufacturing interface in the product-manufacturing system. This issue is being addressed in a variety of ways. In the aerospace industry, the prac- tice of constructing life-cycle cost models to evaluate alternative conceptual designs is known to produce significant reductions in cost (Shoemaker, 1980~. This practice allows all aspects of the design to be treated as a system, including items such as product and process R&D, acquisition, support, and fuel use. As can be seen from Figures 2 and 3, the relative impact of an improvement in the process can be weighed against the over- all cost. A similar life-cycle view is be- coming popular in the automotive industry (Compton and Gjostein, 1986~. The foregoing discussion suggests that drastic cost reductions should be achievable if the design and manufacturing group is allowed more freedom in the interpretation of the design intent and can evolve, there- fore, significant modifications while main- 95 taining the original design intent. In this way, some of the disadvantageous effects and tunnel visions arising from an early crystallization of the design can be over- come. The usual rather narrow path fol- lowed during detailing of the design of components and parts and their assembly suggests that the creative process of design synthesis and design analysis needs to be better understood. There is no doubt that considerations of manufacturing, assembly, serviceability, maintainability, and use of the part or com- ponent during its entire life cycle would benefit from intensive functional and cost- effective designs. Organizations that pro- mote such interaction are known to pro- duce outstanding products (Whitney et al., 1988, in this volume). Why some organiza- tions are able to do this well needs to be better understood. The construction of a
96 1' RDT&E AcqulslUon 1 L)fe-Cycle Costs A I E Support 1 /Airframe Assembly aA ~///~1 \ I I Compon: Other Costs I Assomblv , bA i///////// \1 Part Manufacture \ A // \ I : ~ At\ \ Engine \\ \ Assembly \ \ aE ~ \ I \ AIE ha\ Weight and Performance I\ \ Other Costs \, Part Replacement Component\ Rate Assembly \ E ~//////~/~1 \ \ A - embly | Pa~embly Manufacture E ~~ ~ I Material and ~ Other Conts Machining E I/,, ~' ]' ~ ' Material and I Machining Smother Costs dA ~~ \ I Setup and Other Costs Mlillng Cost Per Alrfram4, = aA. bA. cA . dA eE Setup and Other Costs 1 Mlillng Cost I Per Engine = aE . bE . CE . dE . eE I VIJAY A. TIPNIS al, bl, cl, dl = Fractions with respect to the whole at respective cascading stages. I = A, E A = Airframe E = Engine FUGUE 2 Economic opportunity windows during conceptual feasibility testing of a new material removal technology. model and a methodology for describing the life cycle of a part should be the focus of serious investigations. It can be demonstrated that the design intent should be weighed against manufac- turing process realities only within the con- text of the overall mission and the life-cycle costs. Whether the existing manufacturing system constraints should dictate the part design depends on whether the mission re- quires new materials and therefore new or improved processes. The concept of "flexi- bility" of a manufacturing system (Tipnis and Misal, 1985) has become a key element in establishing the degree of freedom that design engineers should be allowed for parts to be cost-effectively manufactured in the system. Thus, the interface between the product design and modern manufacturing processing has become tightly coupled. This area deserves a rigorous investigation. PHYSICAL PROCESSES IN MANUFACTURING From ancient times, implements have been shaped from materials such as wood, clay, sand, fiber, and stone by using tools and processes that have been developed largely by trial and error. These shaping processes were clearly the forerunner of the historical development of manufacturing processes driven by the impetus to improve naturally occurring materials through min- ing and winning, refining, alloying, and other methods. Current manufacturing pro- cesses, which are limited to about 100, can be grouped, as in Table 1, according to the physical processes used to convert the input raw material into the prescribed configu- ration (part of assembly). Most manufacturing processes are in re- ality a series of individual (unit) processes
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS through which the input material is "pro- cessed" until a prescribed configuration is achieved. Each of these unit processes in- volves a series of steps in which material conversion occurs and various supporting activities take place, including, for exam- ple, the positioning of the workpiece, ad- justment of the tooling, or inspection of the part. A proper description of this collection of operations, often referred to as a process- ing sequence, requires an understanding of the technologies involved in each of the units. As mentioned earlier, no unique se- quence of processes can be assumed to exist for creating a part or component. For ex- ample, an automotive connecting rod can be manufactured by a variety of operations Minion Airframe Requirements _ , DO Engine Requirements Weight and Performance . 97 from a cast, forged, powder metal com- pact, or near-net-shape forging or casting. New and improved materials have cre- ated a demand for new and improved man- ufacturing processes. Many applications now demand that materials perform at increasingly high temperatures and high strength levels (Clark and Flemings, 1986~. New applications have created a demand for material processing methods that can shape objects of complex configuration, ac- curate dimensions, and tight tolerances. Some materials are now being processed in a fashion that leads to properties near their theoretical limits. Ingenious combinations of microscopic structure, alloying, reinforc- ing, coating, deposition, and other tech- _ 1 Engine Performance Fual/lb of Thrust 4.1 I Parameters Tire, Pressure l Material PA - T_~ro Resistanoo Low Cycle Fatigue/t:reep Dot ~. ._ or Fuel Deer \ \ FIGURE 3 Influence of engineer performance and weight on fuel-use costs. \ . Fuel A ._ 3 3
98 niques are deployed to "design" a material for a specific application, often with a unique combination of end-use require- ments for strength, ductility, wear resis- tance, working temperature range, corro- sion resistance, and so on. Besides the traditional use of mechanical and thermal energies, many of the new manufacturing processes use chemical, electrical, mag- netic, laser, electron beam, plasma, or combinations of two or more of these en- ergy sources. It is important to recognize, however, that a new manufacturing process rarely displaces a traditional process completely. Instead, each new process tends to fulfill a special need where it is superior in perfor- mance and cost-effectiveness to all other al- ternatives. As new processes have evolved VIJAYA. TIPNIS TABLE 1 Traditional and Nontraditional Manufacturing Processes (a representative list) Process Traditional Nontraditional Material Removal Machining Grinding Punching Material Deformation Rolling Forging Extrusion Wire drawing Forming Material Addition Plating Coating Material Joining Welding Brazing Compaction Material Transformation Solidification Heat treatment Alloying Turn, mill, drill, bore ID/OD, surface, belt Shearing, stamping Plane, rounds, tubes Open-die, closed-die Forward, backward Open-die Sheet, tube, spinning Electro, electroless Thermal spray Electrode, plasma arc Thermal Puddling Casting, lost wax Tempering, annealing EDM, ECM, laser, EBM ECG, EDG, creep-feed Laser cutting, plasma Isothermal Hydrostatic Superplastic, explosive Chemical vapor deposition, powder vapor deposition, sputtering Laser, electron, inertia Electrothermal Powder metal Directional, continuous Laser beam heat treatment Deoxidation, melt High pressure to meet special needs, traditional processes have undergone continued improvements in response to the new materials and the de- mand for higher performance. Thus, it is no surprise that traditional manufacturing processes continue to play a major role in manufacturing. It is increasingly important, therefore, that material processing techniques, whether new or traditional, ensure that (a) the resulting product has the desired end-use properties, (b) the process rate is acceptable for the production requirements, and (c) the total cost including material and pro- cessing is economically justifiable in rela- tion to other alternatives. An understand- ing of the applicability, capability, and processability of each new and traditional method is essential to the productivity,
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS quality control, and economics of manufac- turing systems. Process development, involving the trans- lation of the laboratory research on process design into a full-scale production process, has traditionally evolved along an experi- ence learning curve. Consequently, costly trial-and-error procedures are frequently repeated. Few academic researchers have been attracted to investigating the techno- logical and economic problems of produc- tion scale-ups of discrete parts manufactur- ing processes. An increased interest in and attention to these problems is clearly war- ranted. MODELS OF PHYSICAL PROCESSES An improved understanding of the capa- bilities, constraints, and limits of processes can be of significant importance in improv- ing the quality, productivity, and cost re- ductions of manufactured parts. Before the 1850s, process knowledge resided within the expertise of artisans. Little formal doc- umentation existed for this information. More recently, attempts have been made to understand and document physical phe- nomena that involve processes. These ef- forts have generally been of two forms: phenomenological investigations aimed at basic understanding of the process and em- pirical investigations aimed at determining the best operating conditions for a given process. Process Knowledge Despite the progress on methodologies for process modeling, most process knowledge remains locked in the expertise of a few individuals associated with the process. In many cases there is little phenomenological understanding of the process. The real chal- lenge is how to extract this knowledge and reconcile it with phenomenological and empirical insights. What does not work is often more useful than what works. Until 99 the advent of expert system methodologies, no systematic approach was available. This know-how consists of · Rules of thumb learned from experts, from peers, and by trial and error, · "If A then B" rules or knowledge or alternative frames of reference; and · Observations of catastrophic failures and limits of a process. Process knowledge extraction and presen- tation for real processes require a close part- nership between an expert practitioner and a process researcher experienced in knowl- edge engineering. The potential benefits of such models in designing, developing, planning, optimiz- ing, and controlling the manufacturing processes are great and are the basis for much of the discussion in subsequent sec- tions of this paper. Phenomenolog~cal Process Models Phenomenological models are constructed to describe the cause-effect relationships be- tween the basic input variables and the out- put variables of a manufacturing process. The drive toward creating phenomenologi- cal models is a natural extension of the be- lief that, since we understand the basic laws of physics, it should be possible to apply these laws and define manufacturing pro- cesses mathematically. Although this has been a desirable goal, there are some for- midable difficulties that have prevented the development of practical phenomenologi- cal process models for manufacturing (Ford, 1966; Shaw, 1966; Opitz, 1966). The fol- lowing generalizations can be made about the current status of these models: · The available theories of plasticity, friction, wear, instability, fracture, and catastrophic failure do not readily apply to the extreme ranges of stresses, strains, strain rates, temperatures, and pressures within the working zone of most processes.
100 . The implicit assumption that the ma- terial is continuous does not conform to the properties of real materials, which are non- isotropic and contain nonuniform distribu- tions of inclusions, voids, and multiphases. Minute changes in the composition and mi- crostructure of a material may induce a profound change in its processability. · Most processes are non-steady-state and cannot be treated by the usual steady-state techniques. · Processes are time-varying in that they tend to degrade from self-induced and ex- ternal disturbances, such as vibrations, fric- tion, wear, plastic flow, and kinematic in- stabilities. Although phenomenolog~cal models are not yet sufficiently refined for many prac- tical applications, the insights gained through their investigation have proved valuable for achieving process improve- ments. They can often reveal crucial char- acteristics that will make new process de- velopment much easier or will lead to process. significant improvements in existing pro- cesses. Processing Unit Constraints (Maximum aped, teed, acedentlon, power, temperature, etc.) Process/Work Parameters VIJAY A. TIPNIS Empirical Process Models Empirical models relate process perfor- mance directly to process variables using experimental data from a real process or a closely simulated situation. As shown in Figure 4, the empirical model can be viewed to encompass one or more phenom- enolog~cal models dealing with the specific cause-effect relationships. The approach often followed in creating a process model includes the following steps (Tipnis, 1977a): · Observe a real process or its effects. · Simulate the real process under con- trolled conditions. · Establish cause-effect relationships, if feasible, between the basic process vari- ables and their results using physical laws. · Establish direct empirical relationships between the process variables and process performance. · Predict what will happen in the real · Improve the correspondence between the model predictions and the real process. Cost Parameters Process Mode! . 0~ p%- / \ 1 , \ Charac. CuVStroko ~ Investment Requirements Geometry jr ~ ~ ~ r _ ~ ~ Process Rats Mlcroeconomic Model Interruption ~ J Frequency Mechanical/Metallurgical Constralnts (Friction, wear, fracture, Instabilltles, failures) / \ / ~ r \ Force Defo'rr~atlon Velocity Heat Phenomcnological Model of Matorlal Removal Flow Stress Strain, Strain Rate Temperature Tool Wear Rate FIGURE 4 Relationship between process and microeconomic models. Cost and Tlme Per CuVStroko
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS 101 For construction of empirical models, statistically planned experiments and re- sponse surface methodologies have been successfully applied (Wu and Ermer, 1966~. The status of empirical models is roughly as follows: · The usefulness of an empirical model is critically dependent on the design of the experiments, the selection of the model, and the variance. · An appropriate working region for the model must be carefully defined, since em- pirical models of objective functions as well as constraints must be constructed. · Most empirical models are first- or sec- ond-order equations in log-transformed space, making them useful for controlling a process through software. · Practical implementation of empirical models, although feasible, is yet to be fully exploited. Recently, time series analysis has also been applied with some success to a few machining processes. This approach has not yet made a significant impact on process modeling (Kapoor and Wu, 1980~. PROCESS ECONOMICS The goal of economic models is to create a tool that can be used to determine a set of operating conditions that will optimize the economic objective function within the working region of the process. Process eco- nomics has been investigated since about 1900 (Taylor, 1907~. At the micro level, process economics deals with optimization and control of unit manufacturing pro- cesses. At the macro level, it deals with se- quences of processing units and support sys- tems and can be used to investigate the economics of the entire manufacturing sys- tem. In considering the economics of a specific unit manufacturing process, it is necessary to define a characteristic processing rate and resetting frequency for the work material and the set of operating conditions. Since a typical process is capable of producing con- figurations from a given work material that may affect the properties of the finished part or assembly (for example, a tolerance range, distribution, surface finish, and in- tegrity), the prescribed performance re- quirements impose limits or constraints on the process variables. Also, constraints must be introduced to deal with regions where catastrophic failure in the process may oc- cur for example, tool chatter or instabili- ties that limit the range of operating vari- ables. These constraints define the working region within which the process can be safely operated. Thus, the process rate and resetting frequency are functions of numer- ous operating variables that ultimately de- termine the production rate and the cost of the part or component. Until recently, each process was de- scribed by a separate economic model. Op- timization of the entire system involved tedious sequential differentiation of each variable taken one at a time. Unfortu- nately, this method does not guarantee that an optimum solution will be found. The following discussion of economic optimiza- tion draws heavily on applications to ma- chining operations (Tipnis et al., 1981~. A1- though similar models should be applicable to all manufacturing processes, this remains to be demonstrated. Generalized Economic Objective Function A general economic model for material removal processes is shown in Figure 5. The quantity I represents an objective function that can be transformed into time, cost, or a combination of time and cost (for exam- ple, profit rated by an appropriate defini- tion of the parameters. In this formulation the generalized process rate that is, the material removal rate and the general- ized process resetting frequency functions that is, the tool life are treated as inde-
102 Production Rate, Cost, Profit Rate, Etc. Setup, Load/Unload, Rapid _ Traverse (i.e., Time Standards) . Geometric Model of N/~: Cuts _ Including Air Cutting at Feed Volume Removed, Tool Change Time, ~ ~ ~ ~ ~ Cutter CosVUse I = TO + A1/R + A2 / RT + A3 Cutting-Rate Model R = R(V, F. RD, AD...) Tool-Life Model T = T(V, F. RD, AD...) - Cost of Material Added to _ the Part at the Work Center Constraints: · Cutter Breakage · Machine Tool Emits (HP, F. V) · Machine ToolFl~cture and Workpiece · Dimension, Surface Finish, and Integrity T = tool life (time units) R = material removal rate (volume/time) V = velocity (dIstanceItime) F = feed (distance/revolution) RD = radial depth of cut (distance) AD = asocial depth of cut (distance) HP = power (power units) FUGUE 5 Generalized objective function for a process model of a material removal process. pendent variables. However, material re- moval rate and tool life themselves are functions of operating variables such as speed, feed, and depth of cut. This leads to implicit dependence between the material removal rate and the tool life. Conse- quently, a trade-off function exists between the material removal rate and the tool life. Constraints on the Objective Function The range of allowable values for the variables must be constrained by the physi- cally realizable working region. This places constraints on the maximum and minimum permissible values of operating variables, such as machine speed and feed range, VIJAYA. TIPNIS power, temperature, or pressure. Cata- strophic failure limits from tool breakage and regions of nonpermissible vibrations arising from phenomena such as tool chat- ter also impose constraints. An example of the test data necessary to define cutter breakage constraint for end mills is shown in Figure 6. Empirical models for con- straints can be derived using the same methodology as that for the process models. Optimization Strategies The most generalized optimization pro- cedure involves minimization of the objec- tive function,.I, given in Figure 5, relative to either time or cost. In the case of machin-
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS ~ 03 ing, the minima for both time and cost have been discovered to exist for conditions that are described by the R-T-F curve (Ravig- nani et al., 1977~. The equation of the R-T-F curve can be found by setting the Jacobian of (R. 1) with respect to operating variables such as cutting speed, V, and feed, F. to zero; to ensure that the R-T-F curve represents a trade-off between the maxima of R and T functions, the Hessian should be negative. An example of an R-T-F curve is shown in Figure 7 for a milling operation. Similar curves can be developed experimen- tally for end milling, sawing, face milling, turning, and other machining operations. If the values on the R-T-F curve are within the working region defined by the constraints, the process will be optimized if it operates along either the time or cost minima of the R-T-F curve. If the values on the R-T-F curve are beyond the working region, the optimal strategy would be to 0.010 0.008 o a so 0.006 c IL 0-004 0.002 0.10 0.20 operate at the limits of the working region closest to the R-T-F curve. Furthermore, it has been demonstrated that the minimum processing time always occurs at higher material removal rates than the minimum for cost (Ravignani et al., 1977~. The cost minimum is shown in Fig- ure 8 on the plot of cost versus cutting rate. This approach is promising for other appli- cations. Control Strategies A viable control strategy must maintain the processes within safe operating limits and operate at a prescribed economic opti- mum. The concept of a control strategy is illustrated in Figure 7. Since the R-T-F curve exists within the working region de- fined by the constraints, the correct control strategy should be to operate the process at or near the R-T-F curve. Operating close to - O ~ O 000 \ 00 : l ·11 ~ O - - - - O ~ A - o O Cutter Breakage O No Cutter Breakage - ?-: Zone of No Cutter Brealcage o 0.30 0.40 0.50 Zone of Cutter '~ Breakage - - o - 0.60 0.70 0.80 Area of Cut (AD x RD), IN2 FIGURE 6 Cutter breakage constraint feed rate versus area of cut for a 1-in. diameter, 2-in. flute length, M42 HSS end mill cutter.
104 4340 Steel, 217 BHN, Dia. = 1 in. FL = 2 in. 4 Flute M10 HSS Cutter, AD = 1 in. V= 150fpm 0 50- - - T<20 ma 0.30 As 0-004 R-T-F Curve 0.006 Feed (inchitooth) 0.008 = = Chatter _< Constraint FUGUE 7 Working region defined by constraint and R-T-F for end milling operation; Time, T. in mini Rate, R. in in.3/min. the constraints involves risking catastrophic failures (Tipnis, 1977a, 1977b). Investiga- tions aimed at determining working regions, trade-off functions, and control strategies for different processes that will allow safe operation near these limits should be pur- sued. Traditionally, most discrete parts manu- facturing processes have been controlled 24.50 ~ 24.00 X , _\ 1 _ Minimum Cost I __ _ 23.00 I ~ I I 2.0 2.5 3.0 3.5 R (In.31min) FIGURE 8 Cost versus cutting rate. Minimum cost occurs at 2.5 in.3/min on the R-T-F curve (see Figure 7~. VIJAYA. TIPNIS and guided by a machine operator. During the past two decades, a growing use of nu- merical control in machining operations has relieved the operator of the responsibility of guiding cutter motions during a cut. The operator is, however, still responsible for the quality of the parts produced and often inspects the parts after the process. Parts that fail to meet specifications because the process drifted out of control are either re- worked or scrapped. Gradually, sensor- based process control has been applied to machining processes. As shown in Figure 9, direct size control can be achieved by a variety of methods, including manual (operator) compensation, post-process au- tomated sensing and compensation, or in- process automated sensing and compensa- tion. The most promising process control is through on-line sensing that detects a drift or change in a dimension and makes the necessary compensation adaptively. This is particularly desirable for unattended ma- chining operations. PROCESS DEVELOPMENT The design of a process to achieve spe- cific mission requirements has been well es-
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS 105 tablished within the aerospace industry for at least three decades. A mission require- ment for a fighter plane to reach an altitude of 40,000 feet within 1.5 minutes directly translates into performance requirements for the engine, airframe structure, and avi- onics. These performance requirements can be translated into targets for working tem- peratures and pressures for the turbine and combustion chamber of the jet engine, for the structural strength and integrity of the airframe, and for the data and information needs of pilots and navigators. This devel- opment of material and structural require- ments from the mission requirements has led to the practice of "designing" materials with the required combination of proper- ties. The concept and practice of creating a process to make the prescribed configura- tions from the "designed" material has evolved through interaction between mate- rials scientists and manufacturing engi- neers. When new materials are involved, it is often discovered that the available pro- cesses are inappropriate or incapable of producing the prescribed configurations from the newly developed materials. A1- though the importance of close collabora- tion between materials and materials pro- ON-~INE cess development has been well recognized within the aerospace industry, academic training of these two closely related groups still occurs in different disciplines. The materials, metallurgy, and ceramics de- partments continue to be separate from the materials engineering and mechanical en- gineering departments. A mechanism for close cooperation between industry and the university research community is needed to enhance designing of new processes. Typical steps needed to translate mission requirements into detailed process plans are shown in Figure 10. Strategic activities dominate the process design and develop- ment process in the early phases. At the top of the figure are shown the different eco- nomic models that can be used in the devel- opment of the design and manufacturing process. Development of New Processes In this section, the issues and challenges in process design and development are ad- dressed, with a primary focus on process and economic moclels. No attempt is made to cover other pertinent aspects, such as the role of materials development and broad manufacturing system issues. POST-PROCESS Control Am- I sensor I Indirect Slze-Control Produced Parts _ 1 ~~? 0 1° O Ol ' ' | Automated 1 1 l ~ Nor I l l l {I AL Direct | Automated Sensor FIGURE 9 Different methods of size control (Novak, 1980~. Slze-Control Manual
106 VIJAYA. TIPNIS 1 lo: ll | Mlsslon | req. l _ Conceptual design - _ 1~ ~ 0 1 ID 0 or ID lo; Prollminary Final dwell part design part design Materials consideration Manufacturing consideration D - lgn/cost trade-off Manufacturlng/cost t.ad.-On TIME ~ 3 1 ~ 1 ° 1 ~ o 1 o ° 1 ° ~ 1 prollminary | I part d awing l Pre I frock planning _ ~ Final p; Irt drawing 1 Detalled process planning FIGURE 1O Design and manufacturing process development. Traditionally, most process development projects have concentrated on the establish- ment of conceptual and technological fea- sibility. Although this appears to be a logi- cal approach, it is not uncommon to find that a substantial research and develop- ment effort results in a process that is not economically feasible and hence cannot be implemented in production. An approach that allows an early evaluation of economic feasibility during the conceptual and tech- nological stages is shown in Figure 11. The inputs and outputs to each stage are shown on the left and right of the boxes, respectively, and the constraints are shown at the top. The first two boxes on the left depict the development and establishment of the conceptual feasibility that is typical of basic research projects. At these early stages, economic opportunity windows can be established from known or potential ap- plications. The next three stages reflect the establishment of the technological and eco- nomic feasibility and the first production implementation of the process. In establish- ing the technological feasibility, it is neces- sary to demonstrate that the process is fea- sible within the technological constraints. Establishing the economic feasibility of the process demands that the economic range of operating conditions be known and used to influence the establishment of the direc- tions for process development. For evaluation of economic feasibility, the following two criteria must be satisfied: · The necessary condition: The cost sav- ings per part plus the value of time savings per part must be positive. · The sufficiency condition: To earn a desired rate of return on investment, the sum of the present values (over the periods covering the life of the process of cost sav- ings per part plus the value of time savings per part, times the parts per period must be greater than the present value of the re- quired investment. Note that the necessary and sufficiency conditions are applicable to a straightfor- ward one-to-one substitution of processing
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108 units in a sequence as well as to substitu- tions of the entire sequence of processing units achieving the identical product or service function. Llence, these conditions are useful for evaluating the economic fea- sibility of replacement as well as new pro- cesses and products. These two criteria, when expressed math- tically, applicable to micro- and macro- economic models and have been applied to laser-assisted machining, high-speed ma- chining, near-net-shape forging, and pro- cess inspection sensor technologies (Tipnis et al., 1981; Tipnis and Watwe, 1983~. Furthermore, refinement and application of these criteria should become a powerful aid in guiding emerging technology re- search and development efforts. The cost savings of a new process should be evaluated against the best possible cur- rent process, since improvement in the cur- rent process may negate the expected gains of the new process. An ongoing economic feasibility analysis must answer the follow- · ~ sing questions: · What are the economic benefits if the proposed process is found to be technologi- cally feasible? · What are the targets for process re- search in view of the opportunities? · What are true process performance trade-offs? · What is the sensitivity of these trade- offs and cost factors to economic feasibility of an improved existing process? The scope of this paper does not permit an in-depth discussion of this important topic (Tipnis et al., 1981; Tipnis and Watwe, 1983~. Since process research and develop- ment is a time-consuming and expensive ac- tivity, a sound methodology to ensure that the development is proceeding toward an economically viable outcome is essential. Further refinement and application of this methodology should be encouraged. VIJAYA. TIPNIS PROCESS PLANNING Process planning involves the selection of a processing sequence and the operating conditions for each unit process within the sequence that will produce a given lot of parts. The planning can be accomplished only after the initial prove-out of the pro- cess. It presumes knowledge in the form of process models or of the processes them- selves. Once the preliminary part drawing has been sent to the manufacturing engi- neer, the activities of preprocess planning, including cost estimation and make-or-buy decisions, are triggered, as shown in Fig- ures 1 and 10. Although make-or-buy and outsourcing decisions are not shown explic- itly in Figures 1 and 10, these decisions are typically carried out during the preprocess planning stage. A knowledge of a vendor's capabilities to purchase materials or fin- ished components and to provide proper quality control and testing on the items sup- plied are critical in creating a coordinated in-house manufacturing and assembly op- eration with those of the vendor or subcon- tractor. The overall planning system block diagram shown in Figure 1 has been found to be a valid starting point for the develop- ment of a practically implementable com- puter-assisted process planning system (Tip- nis et al., 1979~. In this system, "variant" (group technology code-based) and "partly generative" (a graphic generation of opera- tion sequences) has been accomplished. Although computer-assisted process plan- ning has been an area of active academic research for the past decade, it has not made a sufficient impact on the practice of pro- cess planning in industry. This is because most such investigations either have not ad- dressed planning issues across the entire manufacturing sequence or they have made overly severe simplifying assumptions. Thus, the development of most computer-assisted process planning systems in industry has progressed to no more than a glorified word
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS 109 processing and editing system to aid process planners in interactively composing plans. Since the advent of expert systems, sev- eral researchers have attempted to apply them to the problems of process planning. Despite some interesting possibilities, these attempts have revealed that the expert sys- tem methodology for planning is not as straightforward as it is for more narrowly defined domainssuch as diagnostics that depend on the expertise of a single individ- ual. Also, the means by which the process knowledge is captured remains largely un- explored (Tipnis, 1987~. Important research areas in process plan- ning include · Establishment of a methodology for determining processing sequence from spec- ifications of features, dimensions, and tol- erance of the part or assembly configura- tions; · Application of process models to deter- mine the most economical operating condi- tions for each processing unit, and · Evaluation of the impact of operating conditions of each unit process on the pro- duction rate and economics of the entire sequence. Process planning can benefit greatly from a continuing feedback of data from the manufacturing floor. A relational data-base structure appears to be convenient for cap- turing and reconciling the actual versus planned process operating conditions. A1- though computer-assisted process planning has been a growing area of research and development, the capability to select an op- timal processing sequence and operating parameters remains undeveloped. These topics need to be explored through joint in- dustry and academic research projects. NEXT GENERATION OF MANUFACTURING SYSTEMS Traditionally organized manufacturing shops that produce a variety of parts or assemblies in small lot sizes suffer from long lead times, high in-process inventory, excessive reworking and rejects, and high operating costs. To manage this state of complexity, computer-assisted materials re- quirement planning (MRP) with infinite and finite capacity assumptions has been implemented in a number of U.S. corpora- tions over the past two decades. Also, shop floor data-gathering systems that record the labor hours expended on a given job have been implemented. Although MRP systems assist in achieving economies of scale for material purchasing, they tend to fill the shop with anticipated orders. Experience, however, suggests that unplanned last- minute changes to conform to actual orders have been the rule in most traditional shops. Modern manufacturing systems have evolved from two separate but complemen- tary directions: · The development of flexible manufac- turing systems with automated material handling has evolved from the transfer line and process flow line concepts combined with the flexibility provided by computer- numerically controlled machine tools. · The use of just-in-time, total quality control JIT/TQC) methods in Japan has demonstrated that highly responsive and cost-competitive manufacturing can be done without much capital investment. These two directions have essentially merged into focused factories that consist of responsive and manageable flexible manu- facturing cells and systems. The objective of this section is to identify some of the significant opportunities for research and development related to the role of manu- facturing processes within a modern man- ufacturing system design and operation. Dependence on Human Skill and Attention Manufacturing activities, including ac- tual processing and nonprocessing, have
110 been traditionally dependent on human skills and attention. Most tool and die shops continue to depend largely on human ex- pertise for the control of conventional and numerically controlled (NC) equipment. Traditional fixed automation, such as trans- fer lines, is designed with a built-in se- quence of operations that are performed in a predetermined manner. However, the support functions of tool change, readjust- ment, and maintenance normally depend on manual operations. Although modern manufacturing sys- tems continue to depend on the skill of the operator for designing, planning, monitor- ing, and control, the need for constant at- tention is drastically reduced, thus decou- pling the activities of the operator from those of the machines. This enables the process to continue while the operator is attending to other tasks. In materials processing operations, such as NC machining, once the cut has been initiated, the operator must remain alert to signs of sudden cutter breakage or excessive cutter wear that may throw the dimensions or surface finish out of the prescribed toler- ances. An operator experienced in a process can detect the onset of excessive wear by listening to the sounds and feeling the vi- brations generated during the cut. Experi- enced operators also know that it is difficult to stop the machine in time to prevent cat- astrophic failure of a tool once the failure starts to accelerate. The changes in the chip curl and coloration and in the appearance of the machined surface are sometimes used as indicators of the need to slow down the feed rate to avoid failures. Other manufac- turing processes have similar dependence on operator skill to ensure that the process is kept in control. It is interesting to note that the mundane tasks of chip cleanup, tool loading and un- loading and adjustments, and workplace fixturing are the most difficult to automate to perform flawlessly. These tasks may still require human attention in modern manu- VIJAYA. TIPNIS factoring systems. When one realizes the extent of human attention necessary for successful operation of discrete parts man- ufacturing processes, the magnitude of the task of designing and operating unattended manufacturing systems becomes evident. Manufacturing System Design Much of the justification for modern au- tomation is based on providing flexibility for rapid setups and changeover to different parts in a predefined family of parts. The concept of flexibility as a focused prede- fined degree of freedom in a modern man- ufacturing system can be applied to a wide variety of systems, including small, me- dium, and large lot sizes and high, me- dium, and low production rates. Conse- quently, there is a considerable interest in designing, justifying, and implementing such systems. The cascade of activities in a plant, as shown in Figure 12, can serve as a starting point for designing a modern manufactur- ing system. The term flexible manufactur- ing system (FMS) has come to denote a computer-controlled group of numerically controlled machines linked by pallet trans- port and a load-unload system. Also, tool loading and unloading and tool change ac- tivities are under computer control in some flexible manufacturing systems. Since pre- fixtured palletized workplaces can be loaded or unloaded quickly, the FMS is largely independent of lot size. Whereas the FMS controls the actual production activities, the integrated manufacturing system (IMS) incorporates and synchronizes the prepro- duction activities of process planning, NC programming, and tool and material acqui- sition. The IMS therefore is a "focused 7 7 factory" with all the essential preproduc- tion and production functions required to respond to market demands. The potential benefits of FMS and IMS are shown in Figures 13 and 14, where ma- chine use arid throughput time are com-
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS ]~] ... Plant IMS FMS Manufacturing Sequence / FMS Support / / IMS Support /1 /~/ ~ ~ I Single Processing Unit Slagle Operation / / System Level Proce~lng Unit Level t Operation Level Cut Level FIGURE 12 Activity levels in flexible and integrated manufacturing systems. pared with those of the traditional (as-is) manufacturing system. Note that a prop- erly designed FMS provides a significant re- duction of nonprocessing times, increases the machine use (that is, the actual cutting or processing time), and reduces the shop throughput time. The IMS significantly re- duces preproduction time and hence the to- tal time for completion of an order. FMS and IMS significantly improve the avail- ability of a machine tool to do actual pro- cessing, thus increasing the importance of process optimization and control. As noted earlier, processing that proceeds unattended involves guarding against the risks of cata- strophic failures and reduced performance. The first step in designing an FMS is to reexamine processing sequences and oper- ating conditions of the parts in the target family of parts and to evaluate the impact of changes that the processing technology can have on the time and cost of manufac- turing. Concurrently, it has proved advan- tageous to interact with the product design- ers to evaluate changes and standardization in the part features, dimensions, and toler- ances that may lead to significant reduc- tions in manufacturing cost and also reduce the variety of tooling and workplace fixtur- ~ng. The next step is to evaluate all precut and resource supply activities to determine how these can be performed without delaying the actual processing at the workstations.
112 As-in FMS Implemented IMS Implemented Walting, Operator Inefficlency, Malntenance 1 Setup, LoadlUnload, Etc. Actual Cutting / / / / Add_ ~ Ray Machine Workday FIGURE 13 Comparison of machine use for a traditional, flexible, and integrated manufacturing system. Preparation and coordination have been the two prerequisites of a robust design of an FMS. Another important step is to trade off the cost at each processing unit against the overall system performance. Overall system performance must include measures of pro- duction rate, in-process buffer storage, cap- ital investment for all processing units, and the value of work holding pallets and tools. Finally, the system performance can be im- proved, not by optimizing each processing unit, but by optimizing the processing rate of the bottleneck units and by relaxing the other processing units in the sequence. VIJAYA. TIPNIS Although these manufacturing system design steps are known and practiced by some designers, systematic methodology and design theory are needed. Most design work has been done historically by a few creative individuals in the industry. Most academic research on such systems has fol- lowed proven yet narrow tracks of opera- tions research and computer networks. The crucial role of processing in modern manu- facturing systems is still not well explored. Still another reason to start the manufac- turing system design with the actual process is to avoid the costly risk of obsolescence that is, investing substantial capital in man- ~Preproductlon ~ ~ I Shop Throughput l | ~ ~ Machine Controlled r Nil 1 Asin . _ _ _ Percentage I FMS of Reduction I Implemented Percentage lot Reductlon 1 1 Percentage ~ IMS l l | of Reduction ~ Implemented FIGURE 14 Comparison of throughput time for a traditional, flexible and integrated manufacturing system.
PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS 113 ufacturing processes that may be made ob- solete by a new process or a significant im- provement in an existing process. Flexible Manufacturing Systems: Justification and Implementation The large capital investment and risks involved in FMS have prompted a serious inquiry into the methods of justification and implementation of such systems. A reason- ably cohesive methodology that is generic to all FMSs is evolving (Tipnis and Misal, 1985). The steps involved in this methodology, known as the cost/risk/performance analy- sis, are shown in Figure 15. The starting point for the analysis is a determination of the strategic advantages of the proposed FMS implementation. Among the key stra- tegic issues are capturing, growing, and maintaining a chosen market share; cost, quality, delivery, and service issues ad- dressed through novel product redesign to gain competitive advantage; and realigning the capital and cost structure through new facilities and outsourcing. It is generally found that an FMS that can be justified from a strategic point of view is more likely to be successful than one that is justified purely on the basis of operational issues, such as increased equipment use, reduced process inventory, and shorter throughput time. In performing the analysis, it is first nec- essary to compare the performance levels of alternative FMS designs with those of the current (as-is) manufacturing system and then to determine the potential time and cost savings over the current manufacturing system. At this stage, the microeconomic models for each processing unit and the macroeconomic models for the entire sys- tem must be applied. These steps identify potential savings and risk candidates for implementation of emerging technologies. At this stage, lead-time analysis, simulation of material and data flow, and evaluation of alternative technologies are vital to pro- vide a quantitative basis for comparison of alternative FMS configurations against a more conventional system. The most crucial step in the analysis is the determination of the degree of risks and the remedies that will overcome the risks that are due to both processing and nonpro- cessing activities. As stated earlier, the three types of risks that need to be considered for each operation and activity are catastrophic failure, reduced performance, and obsoles- cence. There is a need to develop a system- atic risk analysis methodology similar to a fault-tree analysis but able to contain vari- ous degrees of failure. Lacking such a meth- odology, the risks are evaluated by sub- jective probabilities obtained through the consensus of experts. Another important step is a comparison of the cost allocations to the processing units and support activities in the FMS. Traditional accounting and financial meth- ods are not sufficiently refined to allow such allocation. Also, the distribution of over- head is traditionally based on labor hours. For automated systems, an allocation based on machine hours would appear to be more appropriate. Because most FMSs can sig- nificantly save processing and throughput time, the value of the time saved from actual processing as well as the value of reduced scrap and reworking should be considered. Most traditional accounting methods do not provide for such time-value- based savings. (clearly, a comprehensive methodology for cost allocation is needed as a part of the FMS economic model. Initial efforts in this direction show that the foundation for a sound economic model requires advances in the economics of technological change, a subject not well explored to date. CHALLENGES AND OPPORTUNITIES The central role that processing plays in the manufacturing system has been empha-
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PROCESS AND ECONOMIC MODELS FOR MANUFACTURING OPERATIONS 115 sized throughout this paper. Although the role of other nonprocessing activities (for example, data flow or material handling) are important, one must not forget that the purpose of the manufacturing system is to create a product. Robotics, vision and other sensors, and expert systems have been em- phasized in recent academic research proj- ects. Although these topics do represent ripe areas for exploration, their limited focus rarely allows advances in design and oper- ation of a total manufacturing system. Manufacturing systems have evolved from the traditional functional organization de- rived from the so-called scientific manage- ment of F. W. Taylor and others during the early l900s to today's highly focused facto- ries. The functional organization depended on the efficiencies derived from specializa- tion of labor and machines and standardi- zation of manual tasks. However, the flow of work through a series of functional work- stations made the flow of parts complex and lengthy. The functional emphasis also re- moved the responsibility for overall part quality and throughput from any single workstation operator. Each operator be- came responsible for only the work done at that workstation. Thus, the overall sys- tem the combination of all elements of the transformation process from the initial de- sign to the final product was sometimes not visible. Throughout this paper, con- cerns have been noted with the current un- derstanding of the physical processes, with the models of the process, with the econom- ics of the system, and with the overall sys- tem models. The central problem is that the physical understanding of most discrete parts man- ufacturing processes is not sufficiently re- fined for practical application of phenome- nological process models. Not only do these areas deserve more attention, but also dif- ferent ways of attacking the problems are needed. For example, materials and mate- rials processing research and development are frequently located in different aca- demic departments in the university, mak- ing interdisciplinary research difficult. In industry, most of the new process develop- ment focuses on near-term problems, and limited attention is being given to investi- gations of the underlying phenomena. In- creased cooperation between industry and university should lead to the identification of problems of highest priority and should improve the overall effectiveness of materi- als and materials-processing research. Basic research on process models and methodologies must be pursued on an on- going basis. As was noted earlier, it is of great importance that current limitations in these theories be reduced so that they can be more broadly applied for example, to those areas in which the microstructure of the materials is treated in detail and the typical processing conditions of extreme stress, strain, strain rate, and temperature conditions can be accommodated. The state of the art of empirical process models should be advanced to develop a sound methodology for constructing models of practical use. Currently available models have proved to be implementable for pro- cess control and process optimization. However, a strengthened mathematical ba- sis is needed for constructing algorithms of objective functions and the constraints for a variety of materials-processing opera- tions. Furthermore, it should be recognized that the best quality control is to control the process so as not to produce parts and assembly configurations outside the pre- scribed quality limits. Research is needed not only to develop on-line and look-ahead sensor-based process control but also to es- tablish process control strategies and algo- rithms based on tolerance requirements for parts and assemblies. Influencing the design to achieve period- icity is an important unsolved problem in those instances in which the essential com- munication media are drawings that can- not, as yet, be guaranteed to be free of ambiguity, incompleteness, or inconsistency
116 of features, dimensions, and tolerances. Process capabilities also need to be captured in representations suitable for establishing process alternatives and design and manu- facturing cost trade-offs. The related subject of computer-assisted process and operation planning has at- tracted much attention for the past two decades. Much work remains to be done to make it possible to generate process and op- eration plans from basic principles. Captur- ing processing expertise that cannot be for- malized into mathematical models is still a major challenge. The economics of unit manufacturing processes and sequences and manufacturing systems is finally receiving the attention it deserves. A firm foundation is needed to establish economics as the guiding force for design, development, planning, optimiza- tion, and control of processes and manufac- turing systems. Economic criteria should also be able to identify those new processing technologies that offer the greatest opportunities for ad- dressing the challenges of the l990s. To make wise use of the limited research and development resources and talents for the selected few opportunities is the key to be- ing competitive in manufacturing in the l990s. REFERENCES Bjorke, O. 1978. Computer-Aided Tolerancing. Trond- heim, Norway: Tapir. Clark, J. P., and M. C. Flemings. 1986. Advanced materials and the economy. Scientific American 255(4) :51-57. Compton, W. D., and N. A. Gjostein. 1986. Materi- als for ground transportation. Scientific American 254(10):93-100. Ford, H. 1966. Unsolved problems associated with the forming of metals: Metals transformations. Pp. 193- 210 in Proceedings of the 2d Buhl Conference, W. W. Mullins and M. C. Shaw, eds. New York: Gordon & Breach. Hillyard, R. C., and I. C. Braid. 1977. The Analysis of Dimensions and Tolerances in Computer-Aided Mechanical Design. Computer Laboratory, CAD VIJAY A. TIPNIS Document No. 93. Cambridge, England: Cam- bridge University. Kapoor, S. G., and S. M. Wu. 1980. DDS with appli- cations to manufacturing processes. Pp. 403-414 in Advanced Manufacturing Technology, P. Blake, ed. International Federation of Information Processing (IFIP). Amsterdam: North-Holland. Light, R. A. 1979. Symbolic Dimensioning in Com- puter-Aided Design. M.S. thesis. Massachusetts In- stitute of Technology. Novak, E. 1980. Adaptive Control. Ph.D. disserta- tion. Royal Institute of Technology, Stockholm, Sweden. Opitz, H. 1966. Unsolved problems associated with metal removal operations: Metal transformations. Pp. 261-305 in Proceedings of the 2d Buhl Confer- ence, W. W. Mullins and M. C. Shaw, eds. New York: Gordon & Breach. Ravignani, G. L., V. A. Tipois, and M. Y. Friedman. 1977. Cutting rate-tool life functions (R-T-F): General theory and applications. CIRP Annals 25~1~:295-301. Requicha, A. A. G. 1977. Dimensioning and Toler- ancing. Report T-Ml9, Production Automation Project. University of Rochester, New York. Shaw, M. C. 1966. Historical aspects concerning re- moval operations on metals: Metal transforma- tions. Pp. 211-260 in Proceedings of the 2d Buhl Conference, W. W. Mullins and M. C. Shaw, eds. New York: Gordon & Breach. Shoemaker, W. W. 1980. Life cycle cost as a tool in the detail design of advanced propulsion system. AIAA/SME/ASME 16th Joint Propulsion Confer- ence. Smith, B. 1987. PDES First Testing Draft. National Bureau of Standards, A101 Bldg. 223, Gaithers- burg, Maryland. Taylor, F. W. 1907. On the art of cutting metals. Transactions of the American Society of Mechani- cal Engineers 29. Tipnis, V. A. 1977a. Mathematical models and algo- rithms for adaptive control of NC end milling op- erations. Pp. iv-8-iv-18 in Proceedings of the Inter- national Conference of Production Engineering, New Delhi, India. Calcutta: Institution of Engi- neers of India. Tipnis, V. A. 1977b. A strategy for the development of improved machining of steels. Pp. 1-4 in Pro- ceedings of International Symposium on Influence of Metallurgy on Machinability of Steel. Tokyo: Iron and Steel Institute of Japan. Tipnis, V. A. 1987. Computer-aided process plan- ning: A critique of research and implementations. Pp. 295-300 in CIRP Manufacturing Systems Sem- inar, Pennsylvania State University. Tipnis, V. A., and A. C. Misal. 1985. Economics of flexible manufacturing systems. SME paper MS 85-
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