decision support systems

CHAPTER

7

DECISION SUPPORT SYSTEMS

7.0

INTRODUCTION Design decisions are a complex array of diverse and often contradictory cognitive activities. In CE, most work groups get involved directly in decision making and consulting activities. There are probably thousands of design decisions that are made in an ordinary kind of product. Almost every one of such decisions involves some sort of trade off-perfonnance against cost, what one tean1 (or person) wants against another team (or person), organizational issues against technical issues, and so on. Design decisions differ with

each new piece of added inforn1ation, ne\v person, or new issue discovered. Design issues continually change and evolve during every step of the design. This is because design is an open-ended problem. Ordinarily, many solutions to a design problem are possible. The outcome is determined largely by the extent to which a design problem is understood by the work groups and by the process that is applied to solve them, including 3Ps (policy,

practices, and procedures). In many cases, decisions can spread over multiple teams and organizations. If some of these organizations were not part of the original PDT tree structure, this can delay an agreeable outcome. It is hard to resolve the issues early with only a partial tean1 present, since an agreed plan requires everyone's inputs. It is also hard to kick the issues to their superiors, if some teams were not part of an original product development team (PDT) or a CE structure. Decisions made in early stages of design processes have profound affects on later stages as explained in Chapter 2 of volume I. Right decisions, if timely folded in, can produce tremendous savings in the life-cycle cost of a product. Conversely, tl1e price paid for late or \Vrong decisions can be devastating. Early right decisions can ensure business success by en-

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abling the production of better performing, 1nore robust, and more reliable products. This requires early determination of some key part characteristics (KPCs) that make a product ro-

bust and reliable. In traditional organization, however, information on such characteristics (KPCs) is obtained only after a great delay, by which time the product concept is mostly frozen. It is too late then to make any major product design modification decisions. Teams are required to collaborate over long distances, often cross-nationally. Fortunately, distance normally has not been a problem for delay in decision making. Today, remote collaboration can take place with startling accuracy and speed through electronic networks. Decision support environment (DSE) provides a virtual framework for allowing concurrent team members to establish communication with each other early during a PD 3 (product design, develop1nent and delivery) process. This can have a major in1pact on the design if critical decisions about major product modification strategies have to be made quickly and cooperatively.

Technical communication in DSE is much n1ore demanding, even more so than the airline reservation systen1 or the transactions in banking business. Unfortunately, the technical comn1unication and other software capabilities in DSE have not yet caught up. The transactions in airline and banking businesses are fairly routine, quantitative com1nunications. Other comn1unication tools such as video conferencing and the like are in use today, but unfortunately they are unsuitable for engineering collaboration. They are appropriate mainly for conducting business meetings. In engineering collaboration, the work groups need to know, in real time basis, how a set of parameters proposed by a work group affects other sets of parameters whether or not he or she is directly responsible for them. For instance, if a work group is trying to design a mechanism, it is not enough that the mechanism functions kinematically. So1neone may like to know the sweep volume or the trajectory traced if this has to fit into somebody's assembly. If a work group is designing an automobile door panel, it is not enough for the design work group to design it for aesthetic consideration alone. Someone from the variation analysis \Vork group may want to check to see how the parts fit with the body. Someone from the processing work group may want to check for the sheet metal formability of the part. The DFM work group may want to check for DFMA rules, and the structural work group may want to insure integrity of the design from strength and stiffness perspectives. In a si1nilar manner, an evolving product design goes through a number of revisions, a number of CAD modifications, a number of alternative proposals, prototypes, and so on. One then has to sort through the accompanying electronic or CAD file versions to determine the right output (design) from those alternatives that were evaluated. There is a lack of adequate collaborative tools useful for making early product trade-off during DSE. Most existing DSE tools, for example, are not equipped to compare alternative designs (i.e., to identify a good design from a bad design), or to compute design sensitivities. In the absence of such capabilities, and with tight manufacturing schedules, most trade-off studies, in the beginning of a design cycle, are left incomplete when a product is passed on to the next work group. Decision support tools include both manual and computer techniques to aid decision making, analysis, simulation, documentation, sensitivity, optimization, and control of everything in an enterprise. Though there are a number of developments in CAD/CAM and CAE arenas, their DSE capabilities are still inadequate to enable CE. Jn the present fonn, most C4 (CAD/CAM/CAE/CIM) systems are mainly suitable for analyzing a problem or for capturing an explicit, static geometric representa-

Sec. 7.1

Basis of Decision Making

315

ti on of an existing part. They are not suited for altering a part geometry, say using variable dimensions, or capturing its engineering design intent. Some recent C4 systems use parametric 1nodeling, variational design, adaptive modeling, feature-based inodeling or knowledge-based techniques to capture a part life-cycle intent. Such developments are dynamic in nature when it comes down to managing changes. These new C4 systems use the intent-driven techniques to generically capture a product's life-cycle values. In recent years, more and more emphasis is being placed on the use of such intentdriven techniques for decision support during concurrent engineering [Talukdar and Fenves, 1989; and Sapossnek, Talukdar, Elfes, Sedas, Eisenberger and Hou, 1989]. Alternatively, many companies are developing specialized KBE systems (often called expert system.;;) targeted towards providing decision support for a particular product line or a line of product family. Automated Simultaneous Engineering (ASE) is an example of a prototype critic-based system, an expandable library of autonomous programs called critics. ASE was a joint research project between Carnegie Mellon's Engineering Design Research Center and General Motors' Inland Fisher Guide Division, now called Delphi-I [Sapossnek, Talukdar, Elfes, Sedas, Eisenberger and Hou, 1989]. The initial domain of ASE was a window regulator design. ASE consists of four components, a synthesis system and three critics (a tolerance critic, a mechanical strength c1itic, and a kinematics critic). Flexible Organization (FORS) [Papanikolopoulos, 1988] was employed as a framework for integrating critics. A constraint based design language called Design Objects and Constraints (DOC) [Sapossnek, 1989] was used to create a system for a window regulator synthesis.

7.1

BASIS OF DECISION MAKING Decision n1aking can be vie\ved as a process of creating an artifact that pe1forms \Vhat is expected (specified as a set of requirements) in the presence of all sorts of constraints and operating environments that govern its behavior. The constraints can be pre-specified or can evolve during a design (or PD 3) process. The concepts of concurrency as described in volume I and the decision support system that enables concurrency constantly interact, each pushes the other to ever greater heights. The types of decisions that engineers make today to solve design problems are bounded by a spectrum with the cognitive aspect at one end of this spectrum and the progressive aspect at the other end [Finger and Dixon, 1989]. In cognitive-type situations, cognitive knowledge about the problem and its environment helps the problem solver. A CE team identifies an outcome, a pattern, or an hypothesis from a finite set of possible outcomes that any team-member has experienced, which is closer to the functions of the product. In progressive-type situations, however, information about the product and its behavior is unknown. The problem solver is required to follow an explicit method for approaching the solution. 7.1.1 Cognitive Decision Models Knowledge of designs certainly plays a very important role in coming up with a suitable artifact. Depending upon the cognitive knowledge about a product available to a decision

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inaker, design decision may range from cognitive (that can be adapted) to progressive (having less cognitive knowledge). It is known that experienced designers can create bet-

ter designs, and in a shorter tin1e-frame compared to a novice. What is not known is how the cognitive process in such cases works and how it helps in accelerating the decision making process. For example, why don't individual designers seem to be as creative and productive as a tea1n of designers? Even if a vast amount of inforn1ation is available about a product, it is hard to replace the cognitive knowledge the designers have. Most experi-

enced designers can explore alternatives even with n1issing information and lack of data because their knowledge is broader, more general, and abstract. Less experienced designers are not able to explore alternatives as well. Instead, they try to extend their initial ideas

for the new cases. AI techniques such as rule-based inferencing and expert systems that incorporate and capture the experfs knowledge of the experienced designers, can aid the novice (or Jess expeiienced) in decision making. Com1nonly, a cognitive model is stn1ctured with four pieces of information: alten1atives, criteria, knowledge, and belief [Herling, Ullman and D' Ambrosio, 1995] as shown in Figure 7. l.

l, Criteria: Criteria measure the quality of the proposed solution. The complexity of the stated problem is defined in terms of its constituents-inputs, requirements and constraints. In the cognitive decision model, the criteria are chosen from the inputs, require1nents, or constraints, since outputs are unknowns. {Criteria)= f [{Inputs), {Requirements), and {Constraints)]

(7.1)

2. Alternatives: Alternatives are derived from a set of proposed solutions to the original problem. By specifying alternatives as solutions to the problem, the CE teams begin populating the solution space with design alternatives. Population of alternatives is based on the knowledge of the expected solution and the degree of belief (or confidence) that this alternative will meet the stated requirements or would satisfy

the imposed constraints. Arguments for and against alternatives are stated in terms of the knowledge and belief. The stated values for belief and knowledge assert how well an alternative satisfies or will satisfy each of the criteria. In the context of parameters defined earlier, the following relationship holds good: {Alternatives) = f {Anticipated Outputs or Solutions)

(7.2)

3. Knowledge: In the cognitive decision model, knowledge is captured from a participant's personal experience, and his profound knowledge abo~t the level of good-

ness of an alte1native relative to a chosen criterion. The profound knowledge comes from working with other similar parts and his previous knowledge and experience

about the alternative's function. Ten example levels are chosen as shown in Table 7. I to rank the level of knowledge tl1e participants possess. 4. Belief: Belief quantifies the level of surety about an assertion that an alternative will satisfy the criterion. Ten levels are chosen as shown in Table 7.1 to grade this belief. The belief that an alternative will or will not satisfy the criterion is assumed independent of the participant's knowledge.

Sec. 7.1

Basis of Decision Making

317

Measure of goodness of {Alternatives)= f [{Belief}. {Knowledge]] or in shottf[{B}, {K)]

(7.3)

where B is the degree of belief (or confidence) and K is the level of knowledge (or expertise). Table 7.1 gives the numerical values for Band K that a participant can assign. The values depend on the level of confidence and the knowledge that one possesses about the alternative and \Vhether or not the alternative \vould be able to satisfy the c1iterion.

7.1.1.1 Steps of a Cognitive Decision Model In the foJlowing, a procedure si1nilar to a QFD matrix is follo\ved to obtain the cognitive decision model (CDM) matrix. The CDM matrix is similar to a QFD matrix. Like the QFD matrix, CDM has eight rooms, four line vectors and four 2-D matrices. The line vector corresponding to lVHATs row contains the list of criteria. The HO\Vs co1un1n lists the al1erna-

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7.2.5

Heuristic-based Approach

Heuristic-based approach is ve1y similar to model-based approach except the needed knowledge is derived from heuristics (see Figure 7.3e). Heuristics are simple rules of thumb that many organizations have developed from their experiences or from intuition of what has been found to work well and what has not. The approach computerizes these

Decision Support Systems Chap. 7

328

rules as a part of a DSE for the baseline system. The output reflects the accuracy of such decisions stored in the database. Most heuristic-based approaches lack the continuum nature of paran1eter definition that notmally co1nes from analytical techniques or modelbased systems. Expert or rule-based systems are examples of such hemistic-based approaches where an extensive taxonomy of rules (knowledge-base), along with an inference inechanism, provides the basis for decision making.

7.2.6

Algorithm-based Approach

Algorithm-based approach is an extension of the analytic-based approach, where an algorithm-based feedback loop (in place of analysis) is present as an integral part of the decision making. The iteration is shown in Figure 7.3f. Compared to the analytic-based approach, this is an iterative process where design n1odification is based on sensitivity of petforrnance calculations with respect to problem parameters. A feedback loop can be created using any suitable search techniques to automatically guide the PD 3 process. Such techniques include linear progran1ming, nonlinear progran1ming, integer or mixed integer, dynan1ic programming, and so on. For this reason, most classical optimization models, if applied to a baseline system and coupled with an analysis or simulation, can be categorized as algorithmic based.

7.2.7 Hybrid Approach In real practice, however, individual approaches (sections 7.2.2 through 7.2.6) are not enough to model every level of complexity in solving difficult multi-criterion problems. For instance, most physical problems cannot be modeled using analytical techniques alone. Many behaviors cannot be quantified and are, at best. approximated. Due to the complexity and uncertainty of the design path, it is often difficult to specify design objectives precisely. Since specifications are not quantitative, they do not lend themselves to the analytical method. A combination of progressive approaches is generally needed to aid in reaching the solution. For example, when the problem is ill-conditioned, a combination of heuristic-based and manual-based or combined (analytic-based +manual-based+ heuristics-based) models are employed in reaching an effective decision. However, a vast majority of literature recommends using the analytic-based approach as the primary design inethod. There is a need for further research to explore cross-fertilization possibilities with other progressive approaches. A recent trend in heuristic approach (expert systems) has been its integration with analytic- and algorithmic-based systems (in an objectoriented sense) to make the applicable source of knowledge transparent. Progressive Model :J {Model-based, Manual-based, Experimental-based, Analytic-based, Heuristic-based, Algorithm-based, and Hybrid-based}

7.3

(7.10)

INTELLIGENT MODELS Most of the CAD/CAM, CAE, FEA/FEM, mold flow, costing, value engineering, optimization, analysis, and simulation tools serve only a limited PD3 purpose. Independently

Sec. 7.3

Intelligent Models

329

they are incapable of addressing the challenges that are listed in the next section. For example, the traditional CAD approach offers a static (explicit geo1netry capture or visual aid capability) to the work groups for the documentation of a preconceived part or an assembly. CAD/CAM tools provide a slew of functions for defining geometry (such as surfaces, curves, and boundaries) explicitly. The traditional FEA program (e.g. Naslran) checks the structural integrity of a preconceived pa1t to \Vithstand a given static or dynamic load. Traditional optimization programs (e.g., NASTRAN-OPTI), for instance, are suitable for minimun1 \Veight design of structures. lf the objectives are different from tninimun1 weight design, co1npanies cannot use such packaged progra1ns. Most expert systems can process syn1bolic infonnation and conduct heuristic inferences, but their proble1nsolving ability depends mainly on inferences not on algorithrns, such as what is commonly found in n1ost optin1ization tools. There are many similar issues that limit rhe applicability of most C4 tools. For example, structural perforn1ance is one of the many possible design criteria used. Perforn1ance parameters and criteria differ froin one line of product to another. Some of the criteria are rules of thumb or heuristics, others are algorithtn-based. When the behavior of the system is unknown, experin1ental data, instead of analysis or si1nulation, are often used as alternatives. In any company, over time, a vast a1nount of knowledge about key products is available. Ho\vever, useful data are in proprietary forms and are frag1nented across various work groups. With knov.,1ledge-based systen1s, especially in a \VOrkstationbased client/server computing environment, it is possible to search for designs across the vast engineering repositories. The design search 1nay spread across 111any cooperative work groups or reposito1ies that could ineet functional specifications. In inte11igent models, the rules con1e fron1 such divergent sources as company policy n1anuals or standard design handbooks. Anything of importance to the company, the work groups, the customers, or any of the concerned parties may be captured as rules and 1nade available over an electronic net\vork. The auton1ation world today presents a variety of standard and nonstandard (proprietary) options for the creation of texts, graphics, and translators. They are becoming 1nore and n1ore reliable especially for multin1edia documents (text, line-art, photographs, audio, and video) and CAD media outputs.

7,3, 1

Major Challenges

The major challenges faced during a typical PD 3 process are how to accomplish the following tasks. The challenges are how to: • Modify easily and quickly an existing design (or a CAD model) with minimum hu1nan intervention. • Detern1ine rapidly the eftCcts of desired changes on performance. Evaluate several design alternatives.

• Lin1it the use of (or nu1nber of) prototype testing for decision n1aking. Obtain cost and producibility infonnation. • Obtain sensitivity information.


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Chap. 7

• Reduce the number of hardware prototypes. • Visualize quickly the effects of the design changes. • Obtain a feasible or an optimum design. Define any problem parameter as a design variable.

• Use constraints and performance parameters interchangeably in an optimization forn1ulation. Perfonn what if design iterations. Consider manufacturing constraints up-front, e.g., during a conceptual design stage of a PD 3 process. Many CAD/CAM vendors have started, or are in the process of, developing tools based

on dy11a1nic techniques rather than static techniques to manage more and more of the above challenges. Often, such tools employ variable-driven modeling, symbolic processing, and heuristic inference methods to capture the design and development rules, geon1etry, ~nd a taxonomy for a product realization.

7.3.2

Modes of Decision Making

There are two modes in which decision making can take place during a PD3 process-serial and parallel (see Figure 7.4). In a serial mode, decision making is an integral part of an activity. An activity not only entails information build-up but could also include decision making. A work group may need to carry this serial process when it is desirable to suboptimize the design one activity at a tifne as teams go along meeting the specifications gradually. This may be all right if the activities are dependent or if no feedback across the activities is necessmy for product realization. In parallel decision making, all the major outputs of activities are brought together. This way the activities can nm in parallel allowing them to be processed concurrently. The decision making then involve~ determining on a cumulative basis the effects of the resultant outputs (see Figure 7.4 ).

7 .3.3 Capturing Geometry Most conventional CAD/CAM systems supply a rich array of model building tools geared towards expediting the interactive manual tasks. Most of these are directed towards capturing the as-is, that is, a static representation of a given part. Since the most laborious task in 3-D modeling is capturing the as-is geometry, this is quite helpful if the design that was captured does not change. Among the tools that aid this static process are: Surface Generation Tools: Most CAD/CAM systems supply a rich array of model building tools for creating surfaces. The most primitive are the tools to create: • Analytic su1.fGces-These tools are defined by specifying a simple geometric entity, such as cube, sphere, cylinder or cone, whose surface can be described by a mathe-

matical equation.

Sec. 7.3

331

Intelligent Models

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Decision Making Modes (a) Serial and (b) Parallel

• Suiface of revolution-revolving a profile around an axis, • Sivept su1faces-sweeping a surface along a path in space. • Fitted su1faces-specifying an array of points and curves in space through which a surface is fitted. Another class of tools that are available for building complex shapes are: • Skin su1faces-stretching a surface over a series of profiles. • Freedom suifaces-sweeping and revolving a profile silnultaneously. Skin surfaces are useful in defining objects such as boat hulls or wings \Vhose crosssections vary. Freedom surfaces are useful in defining helical objects. such as screws, allo\ving sweep and rotate profiles along an arbitrary path in space and for modeling bent objects such as pipes.


332

Another set of tools for processing existing shapes are: • Trini suifaces--capacity to trim or cut holes in existing surfaces. • Blend .wufaces-to join existing surfaces to eliminate discontinuity, gaps, etc. Solid Generation Tools: Most advanced 3-D geometric modeling tools allow creation of solids from 2-D profiles. These tools might consist of:

• Extrusion along a line. Sweeping along a trajectory. Revolving around an axis of sy1n1netry. Performing Boolean operations on the existing solid shapes. The resulting solid stays connected to its 2-D shape and can thus be altered by altering its 2-D shape. Some class of tools convert a group of smfaces (skin, swept, freedom, etc.) into a solid model. Some CAD/CAM systems propose to bypass 2-D profile routes by automatically parameterizing some types of explicit 3-D geometry (also called automatic 3-D parameterization). Variable-driven modeling techniques, topological constraints, and primitive objects are often required to define and build a 3-D solid. If the possible design changes do not exhibit significant diversion from a product initial geometry, static representation serves as a simple way to capture and build a 3-D solid.

7.3.4

Beyond Modeling

Static modeling is a minimum set of requirements that form a foundation for a DSE system. Manipulating features that are beyond static modeling are more advantageous in supporting 3-D modification goals. These include: Record and Replay Provision: Many geometric modeling systems that have no facilities for defining variable geometry as a set of controlling parameters entail recreating the entire model. Some have a limited provision for pe1mitting user modification of their geometry once a model is completed. Others do provide facilities to store the interactive steps leading to the generation of a finished shape. To facilitate changes, the system maintains a log file that records the steps used by the design work groups. This script can be edited and rerun to create a modified version of the CAD model. Though this record and play provision automates generation of the model geometry and thus saves design work group time, it is a cumbersome process since even a s1nall change requires rerunning the entire script. This can be very time consuming computationally depending upon the complexity of the part and the number of steps involved. There is also no guarantee that the modified part will be compatible with other parts of the model. Tweaking: Some geometrical modeling systems provide a limited set of tweaking features to speed up the model editing process. These operations expedite insertions of local changes in a finished model without its recreation. Examples of such changes are moving a face, deleting, replacing, tapering and subdividing 2-D curves, 3-D surfaces, or 3-D objects.

Sec. 7.3

Intelligent Models

333

Work planes: Many geometric modeling systems allow a design work group to define 2-D work planes at any location and orientation. This facilitates generation of all geometric constructions confined to that work plane. It constrains al1 corners to have the same depth, eliminating the possibility of inadvertently inserting the wrong depth for one of the comers. In this way, the design work group is assured that all geometry that should be co-planar is co-planar. However, the process is temporary and user controlled. There is no guarantee that, in the future, if a work group changes one of the corners, the other corner points will adjust automatically. Copy and Move Features: When an assembly of a component is designed, it requires multiple instances of the same pa1t. For example, for creating an automobile assembly, four identical wheels are required. One way of creating such an assembly would be to individually create four instances of the parts. This could be very time consu1ning and unproductive. At a later date, if changes are needed to any of the wheels, each wheel has to be individually altered. Some CAD/CAM systems allow insertion of parts without duplications. Work groups need to identify the locations and orientations where parts are to be inserted. Though this process saves the work group time to recreate all the individual instances of parts, some syste1ns actually create all these instances and store them in a database occupying a large disk storage space. Associativity: Associativity means design consistency to a larger extent. When work groups make a change to a part, they generally v. ant the rest of the design to be updated automatically. This is analogous to a spreadsheet where every value change is followed by the rest of the spreadsheet reflecting this change. Most general associativity is bi-directional, that is, a change at any PD 3 level propagates both ways-from model-todrawing if an attribute is changed, or from drawing-to-model if the drawing is changed. Variable Dimension Modeling: Variable dimension modeling (VDM) is a type of associative geometric modeling in which changes to a geometric din1ension result in changes to the CAD model. Previously, CAD vendors used to provide VDM capability through their macro programming language structures. Today, most CAD/CAM systems claim to provide some type of this VDM capability. Most typical of these are parametric modeling, variational modeling, adaptive modeling, dynamic modeling, or feature-based modeling. Key differences among them are with respect to the completeness of the associative mechanisms, mechanics of design-intent capture. geometric versus non-geometric modeling, speed of updates, ease of use, and error trapping. 1

7.3.5

Capturing Smarts

The concept of parallelism and feedback control required for concurrent engineering cannot be exploited easily using conventional techniques. Smart tools and concepts, based on variable modeling techniques, symbolic processing and hybrid inferencing (heuristics + algorithmic) techniques, are required to achieve this parallelism and feedback control. Software Prototyping is a concept of virtual modeling through software programming. Each software module captures one aspect of the functional knowledge toward a PD 3 lifecycle process. The software prototyping concept, besides employing many integration and

334


automation methods during the capturing process, capitalizes on dynamic modeling techniques. Careful planning ensures that these prototype models will have the characteristics that provide some form of dynamic (e.g., variable-driven modeling), as opposed to static (e.g., explicit modeling), environment. Variable-driven modeling is an example of the kind of characteristics that are needed for a sound decision support environment (DSE). Each model of the DSE must be based on sound characteristics. Each model must: Identify and capture consensus on problem or product parameters. Utilize an early design evaluation philosophy to bring all engineering up front at the conceptual design stage. Consider analysis or simulation an integral part of a concurrent PD3 process. Employ generic modeling techniques. Employ principles of geometrical similarity for modeling a family of parts. Establish library of parts families. • Utilize a simultaneous engineering philosophy. This is ensured by making provision for simultaneous (as opposed to sequential) treatment of problem parameters, •

• •

•

• •

inputs, requirements and constraints. Exploit the basic characteristics of a products' life-cycle functions that are of generic nature. Classify all designs by form features, such as ribs, fillets, chamfers, holes, slots, bosses, etc. Establish a library of standard features such as button, hole, thread, punch, warp, etc. Employ a solid modeling design approach that creates a 3-D data base that mathematically describes I 00% of the design information. Employ numerical sensitivity for trend determinations. Employ hybrid inferencing techniques (optimization techniques for algorithmic computation and decision making; and an expert system approach for symbolic processing and heuristic reasoning). Establish a library of standard design practices. Utilize model based reasoning and other Al techniques to create the knowledgebase and to capture the expertise of senior designers and planners if some knowledge cannot be obtained analytically.

Smart models of a PD3 system enable independent SBU sub-groups to work in parallel teams, provide the required electronic feedback to the interfacing groups, and share the results with project managers at marked check points. This system substantially reduces the time required for the completion of the design-to-manufacturing (PD3) life-cycle.

7.4 SMART REGENERATIVE SYSTEM Smart Regenerative System is obtained by combining progressive as well as cognitive aspects of product life-cycle functions. Cognitive aspects capture the knowledge and progressive aspects add a systematic structure for the new design evolution.

Sec. 7.4

Smart Regenerative System

335

The smart here means a knowledge-based engineering (KBE) powered application or a computer module. Several types of rules go into creating knowledge in a KBE environment (see Figure 7.5). Captured knowledge typically includes: • The product structure (or something like a bill-of-materials). Rules for reconfiguring or changing the product structure when there are new inputs. Dependencies and relationships among features and parts of the product so changes to one part or feature automatically change those parts and features that depend on it. Functional, physical and geon1etric attributes. • Engineering iules fro1n contributory engineering disciplines (e.g., manufacturability, structural analysis), engineering rules for design optin1ization.

Decision criteria for extracting infonnation from external data bases, including selection of standard parts from company catalogs and parts from feature-based (CAD) design libraries. • Rules for using geometric design data imported from CAD systems (see Figure 7.5). Some of the mies about a product might involve calculating values; for example, fabrication materials might be evaluated based upon the stress the component is subjected to.

They co1nbine with each other to create a design optimized for cun·ent inputs, requirements, and constraints at hand. Obtaining a smart regenerative system depends largely on the problem set that work groups are trying to solve. The more structured knowledge the problem has, the easier it is to implement a regenerative system. If a work group uses GTclassification and analysis tools in the PD3 process, the most benefiting factor is the ease of machining and maintaining the logic. KBE regenerative systems can \vork with external systems. Rules can call on knowledge in extemal databases like the preferred pans' list or supplier parts catalog. The latter is easier to maintain than the embedded rules. An example of an embedded rule would be when a cotnpany uses so1ne new equipment for a part manufacture or uses a new manufacturing process for its manufacture. The co1npany would have to investigate every hierarchical process to locate a part and every hierarchical decision tree to locate the equipment or the process. If a match is found, the appropriate changes can be made, tested, and documented. Until this preferred parts list is updated in the KBE intemally, the process planners will continue to make parts using the old machines and old processes. With an external parts in KBE system list this can be fixed in no time. Another external action could be to communicate design parameters. If the task is to develop a new design that has certain strength or stiffness, the information must come from an external FEA program (NASTRAN, PATRAN, etc.) An input to the FEA and an output from FEA to the KBE system need to be established for the two to work properly. Such procedural steps cannot be eliminated completely if the goal is to achieve 100% automation. This makes the resultant KBE system dependent. To reduce dependency, work groups need to separate the logic from the data and document the reasoning for decisions in a manner that can be reviewed on-line. Many CE teams who want regenerative product design forget that when the knowledge engine in a KBE system is done creating a design layout, work groups still need a de-

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Chap. 7

Generative Product Design

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