**Design for Automotive Panels Supported by an Expert System**

Chun-Fong You, Chin-Ren Jeng and Kun-Yu Liu

Chun‐Fong You, Chin‐Ren Jeng and Kun‐Yu Liu

Additional information is available at the end of the chapter

efficiency (Choi et al., 1999).

http://dx.doi.org/10.5772/52010

#### **1. Introduction** Department of Mechanical Engineering, National Taiwan University

The production process for automotive panels (Fig. 1) has changed dramatically with advances in computer technology. To shorten the automotive development schedule, industry has been using computer-aided design (CAD) and digital model analysis to replace the traditional design method based on human experience. This can reduce the design error rate and improve production efficiency (Choi et al., 1999). Taiwan, ROC **1. Introduction** The production process for automotive panels (Fig. 1) has changed dramatically with advances in computer technology. To shorten the automotive development schedule, industry has been using computer‐aided design (CAD) and digital model analysis to replace the traditional design method based on human experience. This can reduce the design error rate and improve production

**Design for Automotive Panels Supported by an Expert System**

Figure 1. Production process for automotive panels **Figure 1.** Production process for automotive panels

**2. Expert system**

planning.

The stamping die for automotive panels is a cold stamping die; the input is a plane blank; and the output is the panel shape required by a car company. During production, the punch closes the upper mold and lower mold for various tasks. The goal of process planning is to determine how many dies are needed and the content of each die, including stamping direction, tasks, cam © 2013 You et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 You et al; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 You et al; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

type, and other information that is necessary when designing a die.

system to read the digital surface model, and then output the die layout using Java3D.

The entire process from obtaining a panel's file to producing that panel takes about one year, or even much more time. Die design companies that have the shortest schedules and highest quality can occupy a dominant position in the automotive industry.

This study combines practical experience with an expert system, and focuses mainly on preprocess steps and process planning. The system, which is called computer‐aided process planning (CAPP) (Marri et al., 1998), is programmed in Java language. The system uses the Spring Solid System developed by the Solid Model Laboratory, National Taiwan University, as the backbone of the CAD

The concept of artificial intelligence (AI) was proposed in the 1980s, and the processing method for computer information is evolving toward that of the human brain. Because many difficulties are associated with the use of AI, an expert system is used to solve problems in particular fields. Generally, it can provide such information as the judgments of experts. Unlike Dynavista and CATIA/VAMOS, which are expert systems developed for die design, no software exists that uses an expert system for process The entire process from obtaining a panel's file to producing that panel takes about one year, or even much more time. Die design companies that have the shortest schedules and highest quality can occupy a dominant position in the automotive industry.

**1.** Knowledge database: This database stores such knowledge as empirical rules, analyzed

An expert system mainly consists of a reasoning engine, knowledge database, user interface, and developer interface (Fig. 2).

Users

http://dx.doi.org/10.5772/52010

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Design for Automotive Panels Supported by an Expert System

1. Knowledge database: This database stores such knowledge as empirical rules, analyzed cases, parameters, and other

2. Developer interface: The developer interface allows experts and system developers to modify the knowledge database and

4. Reasoning engine: This engine uses information from the knowledge database to diagnose questions asked by users and

A reasoning engine is widely used with both rule‐based reasoning (Lau et al., 2005) and case‐based reasoning (Tor et al., 2003; Yuen

The knowledge database of rule‐based reasoning stores reasoning rules. After a user enters problems, the reasoning engine starts to

The judgment rule and boundary rule are typical rules. A judgment rule is represented in the form of "if P then Q," and two types of Boolean and index exist. Judgment by Boolean is used only when two corresponding results exist, and judgment by index is used when more than two results exist (Fig. 4). For instance, a knowledge database contains the rules "if x, then y" and "if y, then

Rule Index: 1

Result 1

Result 3

Rule Index: 3

Rule Index: 2 Result 2

The boundary rule result is limited by multiple number sets. For instance, if the input number is less than 6.0, 5.0 is output, and if

The problem of rule‐based reasoning is that converting knowledge into rules is difficult. Knowledge can be separated into explicit knowledge and tacit knowledge (Polanyi, 1958). Explicit knowledge can be converted into rules explicitly, while tacit knowledge

The knowledge database in case‐based reasoning stores previously analyzed cases. After users enter a new case, the reasoning engine compares it with all previously analyzed cases in case base, and then searches for the most similar case and reasons for

Case‐based reasoning has the functions of retrieve, reuse, revise, and retain, called the 4Rs (Kendal & Creen, 2007). After retrieving the most similar case from case base, the information of this case is reused to the new case, and then the proposed solution is

> The most similar case

revised. Finally, the new case is retained in the case base as a reference for subsequent reasoning (Fig. 7).

Retrieve i

New case

Solutions Reason based on the

Case base

Proposed solution

Revise Reuse

Using these two rules can increase the number of judgment modes for a system, and enhance its reasoning ability.

New case

cannot. The knowledge associated with process planning is almost always tacit knowledge.

Boundary 6.0 8.0 12.0

Result 5.0 9.0 10.0 15.0

**2.** Developer interface: The developer interface allows experts and system developers to modify the knowledge database and reasoning engine from external resources. **3.** User interface: This interface allows users to describe questions through a user-friendly

**4.** Reasoning engine: This engine uses information from the knowledge database to diagnose

A reasoning engine is widely used with both rule-based reasoning (Lau et al., 2005) and casebased reasoning (Tor et al., 2003; Yuen et al., 2003), and other reasoning methods exist such as

3. User interface: This interface allows users to describe questions through a user‐friendly operation.

Knowledge database

Reasoning engine

User interface

et al., 2003), and other reasoning methods exist such as neural networks, genetic algorithms, and data mining.

The knowledge database of rule-based reasoning stores reasoning rules. After a user enters problems, the reasoning engine starts to reason according to rules and outputs its result (Fig. 3).

Reasoning engine Facts Solutions

z." When a user enters "x is true," the reasoning engine will reason that the result of "z is true."

Index

The judgment rule and boundary rule are typical rules. A judgment rule is represented in the form of "if P then Q," and two types of Boolean and index exist. Judgment by Boolean is used only when two corresponding results exist, and judgment by index is used when more than two results exist (Fig. 4). For instance, a knowledge database contains the rules "if x, then y" and "if y, then z." When a user enters "x is true," the reasoning engine will reason that the

Knowledge database

cases, parameters, and other information used while reasoning.

questions asked by users and search for suitable solutions.

reason according to rules and outputs its result (Fig. 3).

Figure 3. Reasoning process of rule‐based reasoning

Rule A

Rule B

**Figure 3.** Reasoning process of rule-based reasoning

Figure 4. Judgment rule

Boolean

Figure 5. Boundary rule

**2.2. Case–based reasoning**

results based on the case (Fig. 6).

Case base

Figure 6. Reasoning process of case‐based reasoning

previously similar case

Find the most similar case

Figure 7. The cycle of case‐based reasoning

Confirmed solution

Retain

the input number is in the range of 6.0–8.0, 9.0 is output (Fig. 5).

Result A

Result B

result of "z is true."

**Figure 4.** Judgment rule

neural networks, genetic algorithms, and data mining.

**2.1. Rule–based reasoning**

search for suitable solutions.

reasoning engine from external resources.

Figure 2. Framework of expert system

External resources

Developer interface

information used while reasoning.

operation.

**2.1. Rule–based reasoning**

The stamping die for automotive panels is a cold stamping die; the input is a plane blank; and the output is the panel shape required by a car company. During production, the punch closes the upper mold and lower mold for various tasks. The goal of process planning is to determine how many dies are needed and the content of each die, including stamping direction, tasks, cam type, and other information that is necessary when designing a die.

This study combines practical experience with an expert system, and focuses mainly on preprocess steps and process planning. The system, which is called computer-aided process planning (CAPP) (Marri et al., 1998), is programmed in Java language. The system uses the Spring Solid System developed by the Solid Model Laboratory, National Taiwan University, as the backbone of the CAD system to read the digital surface model, and then output the die layout using Java3D.
