**2. Expert system**

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 difficul‐ ties 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 Dyna‐ vista and CATIA/VAMOS, which are expert systems developed for die design, no software exists that uses an expert system for process planning.

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

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

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

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

Knowledge database

Reasoning engine Facts Solutions

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

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

Figure 2. Framework of expert system **Figure 2.** Framework of expert system

information used while reasoning.

search for suitable solutions.

**2.1. Rule–based reasoning**

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

Figure 3. Reasoning process of rule‐based reasoning

reasoning engine from external resources.

Users

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

**1.** Knowledge database: This database stores such knowledge as empirical rules, analyzed cases, parameters, and other information used while reasoning.

Knowledge database

Reasoning engine

User interface


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 neural networks, genetic algorithms, and data mining. 3. User interface: This interface allows users to describe questions through a user‐friendly operation. 4. Reasoning engine: This engine uses information from the knowledge database to diagnose questions asked by users and search for suitable solutions.

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 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

#### **2.1. Rule–based reasoning** et al., 2003), and other reasoning methods exist such as neural networks, genetic algorithms, and data mining.

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

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,

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

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 difficul‐ ties 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 Dyna‐ vista and CATIA/VAMOS, which are expert systems developed for die design, no software

An expert system mainly consists of a reasoning engine, knowledge database, user interface,

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

Users

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

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

Knowledge database

Reasoning engine Facts Solutions

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.

quality can occupy a dominant position in the automotive industry.

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

exists that uses an expert system for process planning.

External resources

Developer interface

Figure 2. Framework of expert system

information used while reasoning.

search for suitable solutions.

**2.1. Rule–based reasoning**

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

Figure 3. Reasoning process of rule‐based reasoning

reasoning engine from external resources.

layout using Java3D.

188 Advances in Industrial Design Engineering

**2. Expert system**

and developer interface (Fig. 2).

**Figure 2.** Framework of expert system

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). **2.1. Rule–based reasoning**

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

External resources

Developer interface

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

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 result of "z is true." 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 result of "z is true."

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

Figure 4. Judgment rule **Figure 4.** Judgment rule

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

Boolean

Rule A

Result A

Result B

Rule A

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 input number is in the range of 6.0–8.0, 9.0 is output (Fig. 5). Figure 4. Judgment rule 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 input number is in the range of 6.0–8.0, 9.0 is output (Fig. 5). Boolean Rule B Result B Index Rule Index: 3 Result 3 Rule Index: 2 Result 2

Index

Result A

Rule Index: 1

Result 1

Result 3

Result 1

First, this study uses the left side of a fender (Fig. 8) to illustrate the die layout design process (Fig. 9), including feature recognition, machining center searching, drawing direction optimi‐

Proposed solution

Revise Reuse

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

Rule Index: 1

Result 1

Result 3

Rule Index: 3

Rule Index: 2 Result 2

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

Design for Automotive Panels Supported by an Expert System

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

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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

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

Index

Result 5.0 9.0 10.0 15.0

7

The purpose of feature recognition is to categorize a panel into different sections to establish a bridge between a CAD model and the CAPP system (Zheng et al., 2007) because a panel model without feature recognition is merely unsorted surface data.

The purpose of feature recognition is to categorize a panel into different sections to establish a bridge between a CAD model and the CAPP system (Zheng et al., 2007) because a panel

Drawing Direction Optimization

Process Planning

Machining Center Searching

Preprocess

If the curvature of single surface exceeds a critical value, it is called a bend surface; otherwise, it is called a flat surface. Bend surface whose curvatures in two domains both

exceed critical values is also called a corner surface (Fig. 10).

model without feature recognition is merely unsorted surface data.

7

zation, and process planning.

**Figure 7.** The cycle of case-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

Rule A

Rule B

**Figure 8.** Left side of fender

**Figure 9.** Die layout design process

**3.1. Feature recognition**

Fig. 8 Left side of fender

Fig. 9 Die layout design process

Feature Recognition

**3.1 Feature Recognition**

Rule Index: 1

The most similar case

Rule Index: 3

Rule Index: 2 Result 2

Figure 5. Boundary rule **Figure 5.** Boundary rule

Using these two rules can increase the number of judgment modes for a system, and enhance its reasoning ability. The problem of rule‐based reasoning is that converting knowledge into rules is difficult. Knowledge can be separated into explicit Using these two rules can increase the number of judgment modes for a system, and enhance its reasoning ability. Result 5.0 9.0 10.0 15.0

knowledge and tacit knowledge (Polanyi, 1958). Explicit knowledge can be converted into rules explicitly, while tacit knowledge cannot. The knowledge associated with process planning is almost always tacit knowledge. **2.2. Case–based reasoning** The knowledge database in case‐based reasoning stores previously analyzed cases. After users enter a new case, the reasoning 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 cannot. The knowledge associated with process planning is almost always tacit knowledge. Figure 5. Boundary rule Using these two rules can increase the number of judgment modes for a system, and enhance its reasoning ability. 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

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

Boundary 6.0 8.0 12.0

#### engine compares it with all previously analyzed cases in case base, and then searches for the most similar case and reasons for results based on the case (Fig. 6). **2.2. Case–based reasoning**

base

Case New case Find the most similar case 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 results based on the case (Fig. 6). **2.2. Case–based reasoning** 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 results based on the case (Fig. 6).

Figure 6. Reasoning process of case‐based reasoning **Figure 6.** Reasoning process of case-based reasoning

The most similar case Case base Confirmed solution Retrieve i Retain 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 revised. Finally, the new case is retained in the case base as a reference for subsequent reasoning (Fig. 7). New case 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 revised. Finally, the new case is retained in the case base as a reference for subsequent reasoning (Fig. 7).

> Proposed solution

Retain

Revise Reuse

Case base

Retrieve i

New case
