using knowledge-based intelligent reasoning to support ... - CiteSeerX

Proceedings of the Fifth Asia Pacific Industrial Engineering and Management Systems Conference 2004

USING KNOWLEDGE-BASED INTELLIGENT REASONING TO SUPPORT DYNAMIC COLLABORATIVE DESIGN Allen T.A. Chiang*, Amy J.C. Trappey* and C.C. Ku** * Department of Industrial Engineering and Engineering Management, National Tsing Hua University 101, Sec. 2 Kuang Fu Road, Hsinchu, Taiwan 300, R.O.C.

**Industrial Technology Research Institute, Center for Aerospace and Systems Technology Rm.212, Bldg.52, 195 Sec.4, Chung Hsing Rd. Chutung, Hsinchu, Taiwan 310, R.O.C. [email protected], [email protected], [email protected]

ABSTRACT This paper presents a rule-based intelligent design reasoning approach to support dynamic collaborative design. The architecture of the design knowledge base intends to assist product R&D, facilitate design verification and integrate collaborative efforts among members of the design team, which may include suppliers and customers. To avoid the drawbacks of the traditional sequential product development pattern, which often ignores the requirements in downstream product development and lacks of collaborative integration methodology, a conceptual architecture of Intelligent Reasoning Collaborative Design Platform (IRCDP) is developed. Finally, a design case (i.e. the motor design) is used to demonstrate the knowledge-based reasoning platform at work. Key Words: AI, DSS & Expert Systems

1.

INTRODUCTION

The advance of information technology and Internet applications has transformed the business environment from local competition toward the global marketplace. End users have gradually taken the helm of market courses. The speed of “time to market” becomes the key success factor for businesses to meet changing customer demands. The collaborative design processes must be agile, efficient, and accurate in order to develop the products on demands, shorten the research and development (R&D) cycle, and speed up new product availability. Currently, the product design data stored in the product data management (PDM) systems are usually in proprietary forms that are very difficult to be utilized for other purposes by other applications. For an enterprise focusing on R&D, the most valuable intellectual assets are the design know-hows and experiences accumulated during the R&D processes. Unfortunately, most knowledge is hardly shared in any format in PDM or CAX systems. It will be very valuable if

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design engineers can efficiently conduct and evaluate designs based on design knowledge and collaborative experiences extracted from the previous design experts. This research intends to overcome the inefficiency of computer aided design tools, and to develop the design knowledge reasoning system. The mechanism of an expert system inference engine is developed to verify design validity, to resolve design conflicts, to integrate the collaborative process of product development, and to avoid losing design capability (both knowledge and experiences) due to the personnel changes in R&D team. This paper presents a framework of an intelligent reasoning collaborative design platform (IRCDP) to facilitate collaboration between design teams. Design engineers can implement product development and design verification simultaneously through the web-based reasoning platform. During various design stages, design problems can be discovered via design verification mechanism. Product designers can obtain valuable and consistent suggestions of design parameter values. Therefore, designers can eliminate design errors and avoid design conflicts, which are difficult to prevent using the traditional sequential product R&D approach. Finally, a design case (i.e., the motor design) is used to demonstrate the knowledge-based reasoning platform at work. 2.

LITERATURE REVIEW

Hsu and Liu (2000) reported that initial design decisions account for more than 75% of final product costs. However, traditional CAD technology is well suited for the stage of detailed design by individual design engineers. It cannot support other design stages and the collaborative activities during the design processes. Owing to recent advances in the field of artificial intelligence and information technology, the opportunity of applying reasoning in design process have greatly increased. Knowledge-based systems and Internet supporting design activities have been an active area of research. Changchien and Lin (1996) present a framework of concurrent engineering for DFA and DFM. The framework adopts a knowledge-based design critique system for manufacture and assembly of rotational machined parts at the early design stage. The knowledge-based is used to implement the rule-based system. Zhang et al. (2000) propose a knowledge-based functional design automation system. The knowledge representation scheme combines rule-based and object-oriented representation methods to represent functions and function related design characteristics in an intelligent design environment. Tor et al. (2002) define a behaviour-driven functional modeling framework for functional design of mechanical products based on a rule-based causal behavioural reasoning step to guide the design process. Tu and Xie (2001) depict an information methodology to support intelligent concurrent design and manufacturing for sheet metal parts. Zhang et al. (2001) introduce the concept of knowledge-based conceptual synthesizer to support the synthetic phase of conceptual design. By use this knowledge, physical behaviour can be derived from a desired function and a functional model, representing causal relationships among functions and behaviours, can be

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created. Zhang and Xue (2002) introduce a feature based distributed database and knowledge base modeling approach for concurrent design. By using virtual rule-bases in knowledge-based reasoning, the knowledge at remote sites can be employed for product development at a local site. This approach provides a platform for developing the next generation CAD systems with concurrent design capabilities. In O’Sullivan’s research (2002), an interactive constraint-based approach to supporting a human designer during engineering conceptual design is developed. Using such a approach, human designers can be assisted in interactively developing and evaluating a set of schemes that satisfy the various constraints imposed on the design. Wang et al. (2002) point out the paradigm of the design activity is changing drastically, as the use of Internet and because of globalization. Wu and Liu (2003) propose an overall architecture of a Web-enabled PDM system in the collaborative design environment. The architecture is based on the use of open data standards to allow users on a wide variety of platform to access the product data and other related information. It enables product visualization using interactive 3-D graphics by the disparate members of a collaborative PDM team. Zhan et al. (2003) present a web-based collaborative product design platform which enables authorized users in different geographical locations to have access to the enterprise’s product data stored at designated servers and work on them simultaneously and collaboratively on any operation systems. The platform bridges the gap by providing an easy and affordable solution for working under the dispersed manufacturing environment. From the above literature review, it is obvious that most researchers focus on conceptual design, concurrent design and PDM to support collaborative design. However, there are few published works in finding solutions to support dynamic distributed collaborative design team using artificial intelligence and design knowledge. 3.

CONCEPTUAL ARCHITECTURE OF IRCDP

This research first defines the architecture of an intelligent design platform, IRCDP, to support dynamic collaborative design. The architecture enables knowledge building and knowledge reasoning while design projects are conducted with many partners collaboratively. To avoid the drawbacks of the traditional sequential product development pattern which ignores the requirements in downstream product development and lacks for collaborative integration methodology, a web-based IRCDP server is developed. IRCDP can detect design problems and conflicts. It also provides constructive suggestions and feasible design parameters by combining the inference ability of an expert system and a knowledge base. 3.1 Overview of IRCDP Figure 1 shows the conceptual architecture of IRCDP. The architecture is divided into three parts, i.e., system administration, knowledge building and knowledge reasoning. IRCDP consists of six functional modules, which are (1) the administration and authorization

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management module, (2) the knowledge base module, (3) the rule parser module, (4) the rule management interface module, (5) the design reasoning module, and (6) the collaborative design project module. The following sections describe all modules organized in the three main parts of IRCDP.

Figure 1. The conceptual architecture of IRCDP 3.2 Knowledge base The knowledge base of IRCDP includes five knowledge elements, i.e., template knowledge, formula knowledge, knowledge rule modules, design parameters, and product data. 1. Knowledge templates. Knowledge template is a frame-based knowledge representation. Each frame is a data structure that includes all slots describing a particular design knowledge object. Frame representation applies object-oriented concept. This kind of hierarchical knowledge representation possesses inheritance characteristics. Most new product developments are derived from the base model or modified from previous design. Zhang, et al. (2001) point out that object-oriented technique provides the product design flexibility. The ability to mix and combine different objects allows us to generate many design alternatives quickly. Therefore, a frame-based knowledge representation is quite

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2.

3.

4. 5.

suitable to create, manage, and reuse related design knowledge. Knowledge formula. A formula describes the relationship between a dependent variable and a set of input variables. Designers are allowed to manipulate input variables independently. Each variable represents a slot of the knowledge frame. These formulas can be applied in reasoning the optimal parameters of product design and in executing design verification. Design parameters. Product design parameters of the entire reasoning process are defined and stored in knowledge objects of the knowledge base according to knowledge templates. These design parameters are validated by the inference engine, which will examine potential design conflicts and give suggestions of parameter modifications to members of collaborative design teams. Product data. Designers execute design inference by reviewing design drawings and parameter tables. These data help design engineers to achieve design parameter input. Knowledge rule modules. Each rule knowledge module is made up of several knowledge rules to form a complete inference chaining. Rules can be reused by different design projects. Because each project only imports necessary knowledge rule modules, it will significantly promote matching efficiency. Modularized rules make knowledge engineers and domain experts easier to manage and maintain domain specific knowledge base.

3.3 Rule management interface This paper develops an Internet-based rule management interface. By utilizing web browsers, knowledge engineers can intuitively use the natural language and graphical user interface (GUI) to manage design rules in real time and respond design requirements dynamically. Figure 2 shows a functional framework of the rule management interface. The paper proposes a concept of knowledge rule modularization. Each module forms an entire inference tree and provides a schematic view by using the tree structure to manage, maintain rules. In the antecedent of the rule construction aspect, we use slots of knowledge objects to create logical conditions of the design variables. In the conclusion of the rule construction, except for declaring the values of design variables and executing formula operations to attain optimal design parameters, the framework, which satisfies dynamic collaborative design requirements with communications, provides e-mail sending to the members of distributed design teams. This function makes distributed collaborative design teams understand the conditions of product development or the conflicts of product design.

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Rule management functions

Modify rules

Construct rules

Display the structure of a rule

Select knowledge templates

Modify conditions

Modify the structure of conditions

Define the antecedent Define conditions

Define variable conditions

Delete rules

Define conclusions

Declare the values of design variables

Define mathematical conditions

Define actions

Define the interactive functions

Execute formula operations

Define the e-mail function

Combine conditions

Figure 2. The framework of rule management functions

3.4 Collaborative design project IDCDP provides project managers to create a collaborative design project. According to requirements of each project, project managers select knowledge modules and set the necessary parameters of product design before reasoning. The designers of distributed collaborative design teams can execute design validations to detect the potential conflicts of design parameters and find ideal design parameters. Figure 3 shows the functional framework of collaborative design project.

Figure 3. Management functions of the collaborative design project

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3.5 Design reasoning (inference) The inference engine matches design parameters with knowledge rules. If the antecedent of the knowledge rule is satisfied, then it will be activated. The inference mechanism of IRCDP uses a Java Expert System Shell (JESS) developed by Ernest Friedman-Hill at Sandia National Laboratories (2001). JESS is designed to support the development of rule-based expert systems, which can be tightly coupled with other Java applications, and uses object-oriented technology to represent knowledge. Therefore, JESS is well suited to develop an integrated knowledge representation and to support dynamic collaborative design inference via Internet. 3.6 Rule parser In order to let knowledge engineers focus on the accumulation and transformation of original design knowledge to knowledge rules, the GUI is designed in the natural language way. The knowledge rule parser plays an important role. It can transfer the natural language displayed by using the infix order into JESS format represented in the prefix order. 3.7 Administration and authorization management Nowadays, owing to the increasing complexity of product design, there has been a shift in product development paradigms. A single company with co-located design teams can no longer accomplish the entire product development. In order to address the needs of the new product development paradigm, IRCDP allows team members geographically come from different disciplines and expertise. IRCDP creates collaborative design environment to let different members of teams participate through Internet. According to different roles, IRCDP sets the following levels of authorization: z System administrators. This role has the most authority of those who are in charge of the system. z Project managers. The project manager can create a new product development project, select knowledge rule modules, decide the members of collaborative product design and monitor the entire product development z Knowledge engineers. Knowledge engineers are responsible for managing and maintaining the entire knowledge base. z Product design engineers. Design engineers use the design inference to find feasible design parameters and detect design conflicts.

4.

CASE STUDY

In this section, the V type belt design – applying in an air compressor development as a case study is introduced to demonstrate the functions of IRCDP. The V type belt design is decomposed into two parts: (1) to design the dimensions of V type belt, and (2) to design the

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power driven system of V type belt. In the case, there are four parties, including customers, the power design company, the dimension design company and an air compressor manufacturer, participating collaboratively in product design. Figure 4 shows an architecture of the V type belt collaborative design. Firstly, according to the customer needs, such as the usage time, the use condition and the adoption of standard part to reduce the cost, the air compressor manufacturer will transfer the requirements into initial product specification. Design experts that come from the power design company and the dimension design company, or Knowledge engineers input knowledge rules into knowledge base through the interface of knowledge rule management. The project manager creates a new project for the V belt design applying in an air compressor development and then chooses the needed knowledge rule modules. Once the design project is created and started, the members of the distributed collaborative design teams begin to execute the design project. Design participants input necessary design parameters to reason the optimal design parameters and detect potential design conflicts. Figure 5 shows the design inference process of the V type belt design using Unified Modeling Language (UML). In the V type belt design case, each design inference step needing knowledge rules has been developed, some of which are formulated in Table 1. When IRCDP detects any design conflict, it will inform the project manager and the related design members, and give some suggestion for solving the design problems. Finally the design project finishes the prototype of V type belt applying the air compressor as shown in Figure 6.

Figure 4. The architecture of the V type belt collaborative design

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Table 1. Example rules for the V type belt design Step

IF

THEN

number 1

2

Overload coefficient = 1.2 AND The power of a driving

Design power = 6.6 (Execute

motor ≠ Null

the formula operation)

5< Design power < 6 AND The rotational speed of a

The type of V belt = B

driving motor =1750 3

The power of a driving motor = 5.5 AND The rotational

The diameter of a driving motor

speed of a driving motor =1750 AND The frequency of

= 125

a driving motor = 60 The diameter of a driving motor ≠ Null

The diameter of a driven motor = 224

4

The type of V belt = B

K1 = 5.4974 AND K2 = 2.7266 K3 =1.9120*10-8

5

The rotational speed of a driving motor ≠ Null AND

Rotatory ratio = 1.79 (Execute

The rotational speed of a driven motor

the formula operation)

1.52 < Rotatory ratio < 1.99

K4 = 1.12.2

K1 ≠ Null AND K2 ≠ Null AND K3 ≠ Null AND K4 ≠

The capacity of transmittimg

Null

power = 2.945

The diameter of a driving motor ≠ Null AND The

The length of a V belt = 1753

diameter of a driven motor ≠ Null

(Execute the formula operations)

6

The length of a V belt ≠ Null

The center distance = 600 (Execute the formula operations)

7

The diameter of a driven motor ≠ Null AND The

The compensative coefficient

diameter of a driving motor ≠ Null AND The center

of the contact angle of a driving

distance ≠ Null

motor (Execute the formula operation)

8

The type of V belt = B AND The length of a V belt =

The compensative coefficient

1753

of the length of a V belt = 0.95

The capacity of transmittimg power ≠ Null AND The

The compensative power of the

compensative coefficient of the contact angle of a

V belt = 2.742 (Execute the

driving motor ≠ Null AND The compensative

formula operation)

coefficient of the length of a V belt ≠ Null 9

The compensative power of the V belt ≠ Null AND

The number of the belt = 2.41

Design power ≠ Null

(Execute the formula operation)

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Figure 5. The design inference process of the V type belt design

Figure 6. The V type belt design – applying an air compressor 5.

CONCLUSION

A knowledge-based intelligent reasoning collaborative design platform (IRCDP) is developed in this research to enable dynamic collaborative design. In this platform, the integrated knowledge representation combining frame-based and rule-based patterns is developed. This

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knowledge representation combines product design knowledge and design rules seamlessly. The knowledge rule parser allows knowledge engineers use natural language to construct, manage and maintain rules easily to satisfy collaborative design requirements. IRCDP also provides interactive function for designers to achieve the design inference using the defined rules. The characteristics of IRCDP are summarized as follows: z The platform provides an integrated collaborative design environment. z Design engineers can efficiently conduct and evaluate product designs. z Designers can avoid design errors and design conflicts. z Design know-hows and experiences can be accumulated. z Knowledge rule modules can be reused by different projects.

ACKNOWLEDGEMENT

This research is partially supported by the R.O.C. National Science Council (NSC) and the Industrial Technology Research Institute (ITRI). REFERENCES Changchien, S.W., and Lin, L. (1996), A knowledge-based design critique system for manufacture and assembly of rotational machined parts in concurrent engineering, Computers in Industry, 32, 117-140. Friedman-Hill, E. J. (2001), Jess, The expert system shell for Java platform, Sandia National Laboratories, Version 6. Hsu, W., and Liu, B., 2000, Conceptual design: issues and challenges, Computer-Aided Design, 32, 849-850. O’Sullivan, B. (2002), Interactive constraint-aided conceptual design, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 16, 303-328. Tor, S. B., et al. (2002), Guiding functional design of mechanical products through rule-based causal behavioural reasoning, International Journal of Production Research, 40.3, 667-682. Tu, Y. L., and Xie, S. Q. (2001), An information modeling framework to support intelligent concurrent design and manufacturing of sheet metal parts, International Journal of Advanced Manufacturing Technology, 18, 873-883. Wang, L., et al. (2002), Collaborative conceptual design-state of the art and future trends, Computer-Aided Design, 34, 981-996. Xu, X. X., and Liu, T. (2003), A web-enabled PDM system in a collaborative design environment, Robotics and Computer Integrated Manufacturing, 19, 315-328. Zhan, H. F., et al. (2003), A web-based collaborative product design platform for dispersed network manufacturing, Journal of Materials Processing Technology, 138, 600-604. Zhang, W. Y., et al. (2000), Automated functional design of engineering systems, Journal of Intelligent Manufacturing, 13, 119-133.

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Zhang, W. Y., et al. (2001), A prototype knowledge-based system for conceptual synthesis of the design process, Journal of Intelligent Manufacturing, 17, 549-557. Zhang, F., and Xue, D. (2002), Distributed database and knowledge base modeling for concurrent design, Computer-Aided Design, 34, 27-40.

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using knowledge-based intelligent reasoning to support ... - CiteSeerX

using knowledge-based intelligent reasoning to support ... - CiteSeerX

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