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Research on hybrid expert system application to blanking technology
Journal of Materials Processing Technology 116 (2001) 95±100
Research on hybrid expert system application to blanking technology Xiaoyi Chi*, Ma Haojun, Zhao Zhen, Peng Yinghong Department of Plasticity Technology, National Die and Mold CAD Engineering Research Center, Shanghai Jiao Tong University, Shanghai 200030, PR China Received 6 July 1999
Abstract This paper introduces the application of a second generation expert system in blanking technology. Depending on the empirical knowledge of domain experts and ®eld literature, an expert system can play an important role in the ®eld of blanking technology. An objectoriented blackboard model is given which can realize hybrid knowledge representation, such as production rules, frameworks and procedures. This paper mainly discusses the structure of the blackboard model and hybrid knowledge representation in blanking technology expert system. Since strip layout is a crucial and determining part in blanking technology, given here is an example of a blackboard model supporting hybrid knowledge representation about the strip layout process. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Blanking technology; Hybrid knowledge representation; Blackboard model; Expert system
1. Introduction Standardization and computerization of die design knowledge is of prime importance owing to the trends in automation and intelligence of blanking die design. As an important sub-®eld of arti®cial intelligence, an expert system has already been introduced into the ®eld of die CAD. Since expert system technology has evolved from ®rst generation to second generation [1], the new technology should be applied to the die CAD system, and researchers should use these advanced technologies in their work. First generation expert systems usually employed a single knowledge representation formalism and they explained their recommendations by rephrasing the rules involved in the inference chain that led to the solution. However, blanking technology design is a complicated, experiencebased process including many procedures such as blanking, bending and local forming, as shown in Fig. 1. These procedures involve an amount of complicated domain knowledge, and so a ®rst generation expert system is dif®cult to meet the requirements of blanking technology design. Second generation expert systems are distinguished from their counterparts of the ®rst generation in the following ways [1]: (1) they organize their knowledge base in modules and multi-level structures; (2) they employ *
Corresponding author. Present address: Department of Mechanical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794-2300, USA. E-mail address: [email protected] (X. Chi).
hybrid representations by combining multiple representation schemes; (3) they supply sophisticated semi-automated and automated tools to help with knowledge acquisition; (4) they can provide more satisfactory and comprehensive explanations of their actions. As a feature of second generation expert systems, they separate control knowledge from domain-speci®c knowledge and organize the knowledge base in multi-level structures. These advantages of a second generation expert system can provide a mechanism re¯ecting practical design process, and will improve the system's intelligence and automation when applied in blanking die CAD. 2. Traditional knowledge representation formalism Simply put, the phrase ``knowledge representation'' means that knowledge about facts and relations is encoded in a manner that allows computers to recognize it. Knowledge representation is very important in building an expert system because the success of the expert system depends largely on the effectiveness of knowledge representation. Most blanking technology design expert systems employ single knowledge representation, the following being the two most commonly used types [2]: 1. Production-rule format. The format can encode knowledge as follows: IF (prerequisite/condition) THEN (action) The following rule is an example:
0924-0136/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 0 1 3 6 ( 0 1 ) 0 0 8 2 4 - X
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X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
Fig. 1. Blanking technology classification.
IF the hole-to-hole distance has precision requirement THEN holes should be pierced at the same step in order to decrease accumulated dimensional tolerances caused by location errors 2. Frame representation. The basic structure can be described as follows: { [ () () . . .. . .] [ () . . .. . .] . . .. . . } Frame representation is widely used in expert systems because its structure is suited to the description of a group of basic concepts and facts. 3. Hierarchical knowledge representation Hierarchical knowledge representation means that concepts can be classi®ed into hierarchical organizations in which the top knowledge is abstract and general while the descendant knowledge is speci®c and is used to solve practical problems [3].
There are two types of knowledge contained in the knowledge base of blanking technology: domain knowledge and control knowledge. Domain knowledge is speci®c to the problem at hand. Control knowledge is more of abstract nature and describes the overall objectives and strategies of how the system is to pursue a goal. In ®rst generation expert systems, no distinction was made between the two types of knowledge and control knowledge would either be absent or implicit and hidden within the domain-speci®c knowledge. Consider the following example: IF there exists two holes whose differences in diameter are great, and the distance between two holes < minimal hole-to-hole distance that will prevent a weak die THEN pierce the large hole first, then the small one There is an important control strategy here which is implicit in the design of the rules. The order of the two conditions of the rule is not arbitrary. The condition ``the distance between two holes < minimal hole-to-hole distance that will prevent a weak die'' should be considered only if ``there exists two holes whose differences in diameter are great''. So, there is an implicit blanking technology hierarchy which determines the rule structure. The control strategy of this rule may be described as follows. Before checking the location, check the diameters. The strategy remains unstated in the knowledge base. However, making such control knowledge explicit can make it more convenient to represent blanking technology and can supply more comprehensive explanations of the system decisions, which
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
97
Fig. 2. Hierarchical knowledge representation illustration.
also attributes to building and maintaining the knowledge base. Take the above rule for example: two conditions locate at different hierarchies. When the top domain knowledge (i.e. there exists two holes whose differences in diameter are great) is activated, the down domain knowledge (the distance between two holes < minimal hole-to-hole distance that will prevent a weak die) is activated through control knowledge; thus the domain knowledge is separated from the control knowledge. The above example is one rule about blanking which is easy to understand. However, the process of blanking is complicated and involves many factors that have to be considered. For example, when piercing holes in the process of strip layout design, many related factors must be taken into account. The related rules are as follows: Rule 1: If the hole-to-hole distance has precision requirement, then holes should be pierced at the same step in order to decrease accumulated dimensional tolerances caused by location errors. Rule 2: If the hole-to-contour distance has precision requirement, then the hole should be pierced at one step before the blanking step as far as possible so that it is approximately close to the blanking step. Rule 3: Pilot holes should be pierced at the ®rst step in order that piloting could work at the following steps. Rule 4: If a hole has high-dimensional precision and contour requirements (especially to thin strip materials), then the hole should be pierced at the back steps in order to avoid the in¯uences of other steps. Rule 5: If a hole interferes with many other holes (i.e. the minimal hole-to-hole distance > permitted minimal value), then the hole should be pierced at the next step.
Rule 6: If there exists two holes whose differences in diameter are great, and the distance between two holes < minimal hole-to-hole distance that will prevent weak die, then pierce the large hole ®rst, then the small one. Rule 7: With the strip material rigidity requirement, pierce the small hole ®rst, then the large one. In the rule-based expert systems, the rules are stored in the knowledge base as text and have no clear and de®nite classi®cation. In this paper, the authors put these rules into hierarchical organizations. Although it has been noted that every person has a different hierarchy of concept which is in¯uenced by one's personal experiences and beliefs, it is still feasible to employ a hierarchical organization of knowledge in the ®eld of blanking technology which has been developed maturely for many years. Fig. 2 gives an illustration of hierarchical knowledge representation. The illustration is a general thought which ignores some details. For example, in Rule 6, more details discussed above here are ignored. 4. Blackboard model supporting hybrid knowledge representation Blanking technology includes many types of knowledge each of which employs different knowledge representation format. Now, it is possible to employ hybrid knowledge representation by applying hierarchical concept to the organization of blanking technology knowledge base and realizing the separation of domain knowledge and control knowledge. This paper gives an object-oriented blackboard model which employs many types of knowledge representation formats
98
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
and forms hybrid knowledge representation under the administration of blackboard. 4.1. O-o technology O-o (object-oriented) technology is an essential ®eld of software in recent years. The main building block of an o-o program is object which simulates real-world concept. O-o technology describes the data structure of objects to build the system, which matches the human's cognitive process. It realizes data abstraction, information hidden and class inheritance between objects. The concept of class re¯ects the human's cognitive process from the general to the speci®c. So, it is feasible to apply o-o technology to the design of expert systems. 4.2. Blackboard model The blackboard model provides a very ¯exible control structure for judging and solving problems. In the blackboard model, information in different processes (or at different levels) is described on the blackboard, then recommending/reference information is output. So, the characteristics of the blackboard is that it is a shared data area, or it is an interactive medium of stored information (data) and
knowledge sources. Blackboard model usually consists of three major components [4]: 1. Knowledge source. Knowledge used to solve problems is divided into many knowledge sources which are stored individually and independently of each other. 2. Blackboard data structure. The blackboard is referred to as a global database that records and organizes intermediate problem-solving data. Knowledge sources produce changes to the blackboard that lead incrementally to a solution to the problem. Communication and interaction among knowledge sources take place solely through changes on the blackboard. 3. Controller. The KSs (knowledge sources) respond opportunistically to changes on the blackboard. The control shells, which lie in KSs, blackboard and isolated modules or the combination of the above three items, monitor the changes on the blackboard and decide which action to take next. A blanking technology design process is characterized by the following requirements: (1) it is a creative process; (2) it contains a variety of information; (3) multiple design results will be given. In order to meet these requirements, expert systems should provide a variety of knowledge representation formats to build KSs base. An object-oriented
Fig. 3. Blackboard model.
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
blackboard model can realize hybrid knowledge representation, such as production rules, frameworks and procedures. According to the characteristics of blanking technology, different types of knowledge are represented in different ways, which are embedded in blackboard model to produce an o-o blackboard model supporting hybrid knowledge representation. In this way, high ef®cient administration of blackboard combined with the advantages of hybrid knowledge representation and o-o technology can be incorporated to the complicated blanking technology expert system design. Fig. 3 shows a blanking technology expert system blackboard model. As shown in Fig. 3, the blackboard is a global database which stores necessary conditional information and solutions recommended by knowledge sources. Interaction among independent KSs must take place through blackboard. When a rule of blanking technology is activated, a message is sent to the blackboard which processes the message and then sends another message to knowledge base. The knowledge source with the highest priority is
99
executed, it will complete one or more of the following procedures: (1) add a hypothesized element to the dynamic blackboard; (2) revise existing elements; (3) produce a link object of some hypothesized element. Then, the control shell in the blackboard will monitor the changed element, activate the correspondent KSs and produce further changes on the blackboard until these changes lead to a satisfactory blanking technology decision. 4.3. An example The processing feature is the main criteria in blanking technology design. Thus, this paper employs hybrid knowledge representation based on hierarchical knowledge representation and takes processing features as objects. In this method, frameworks are used to describe object which includes object name, attributes and operations on object. Production rules and procedures can be combined with frameworks to represent complete and detailed blanking knowledge. Object name represents processing feature,
Fig. 4. An example of the blackboard model supporting hybrid knowledge representation about the strip layout process.
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X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
attributes include the super-class of the object, knowledge type and object class, method means dynamic actions of objects and procedure knowledge processing. Blackboard is a dynamic monitor which processes all types of knowledge and at the same time, its functions include receiving and sending messages and searching matching rules, etc. Fig. 4 gives an example of a blackboard model supporting hybrid knowledge representation about the strip layout process. Operation sequence arrangement is one of the sub-classes of strip layout, being derived from class strip layout. The knowledge is stored at different levels of knowledge sources and is activated by the blackboard event driven by messages [5,6]. 5. Conclusions An expert system for blanking technology is signi®cant in improving die CAD intelligence. However, blanking technology design is a complicated and ¯exible process which makes it challenging to build an expert system. In this paper, the authors describe the characteristics of a second generation expert system, and give an o-o blackboard
supporting hybrid knowledge representation. In addition, how knowledge and experience are represented in knowledge base is also discussed. Since there are many functions for further development, it is possible to enhance the research in the future.
References [1] C. Nikolopoulos, Expert System, Marcel Dekker, New York, 1997. [2] X. Xiangzhi, et al., Blanking Technology and Die CAD, National Defence Industrial Press, Beijing, 1996 (in Chinese). [3] M. Kamel, Y. Quintana, Generation of Concept Hierarchies for Engineering Knowledge-based Systems, Application of Artificial Intelligence in Engineering VII, Computational Mechanics Publications, 1992, pp. 107±124. [4] H. Bo, Z.K. Ni, Structural design of an intelligent decision system generator based on blackboard model, Comput. Res. Dev. 34 (5) (1997) 382±386 (in Chinese). [5] B.N.K. Ann, C.C. Kai, A knowledge-based system for strip layout design, Comput. Ind. 25 (1994) 31±44. [6] J.C. Choi, B.M. Kim, Y. Cho, C. Kim, J.H. Kim, An integrated CAD system for the blanking of irregular-shaped sheet metal products, J. Mater. Process. Technol. 83 (1998) 84±97.
Research on hybrid expert system application to blanking technology Xiaoyi Chi*, Ma Haojun, Zhao Zhen, Peng Yinghong Department of Plasticity Technology, National Die and Mold CAD Engineering Research Center, Shanghai Jiao Tong University, Shanghai 200030, PR China Received 6 July 1999
Abstract This paper introduces the application of a second generation expert system in blanking technology. Depending on the empirical knowledge of domain experts and ®eld literature, an expert system can play an important role in the ®eld of blanking technology. An objectoriented blackboard model is given which can realize hybrid knowledge representation, such as production rules, frameworks and procedures. This paper mainly discusses the structure of the blackboard model and hybrid knowledge representation in blanking technology expert system. Since strip layout is a crucial and determining part in blanking technology, given here is an example of a blackboard model supporting hybrid knowledge representation about the strip layout process. # 2001 Elsevier Science B.V. All rights reserved. Keywords: Blanking technology; Hybrid knowledge representation; Blackboard model; Expert system
1. Introduction Standardization and computerization of die design knowledge is of prime importance owing to the trends in automation and intelligence of blanking die design. As an important sub-®eld of arti®cial intelligence, an expert system has already been introduced into the ®eld of die CAD. Since expert system technology has evolved from ®rst generation to second generation [1], the new technology should be applied to the die CAD system, and researchers should use these advanced technologies in their work. First generation expert systems usually employed a single knowledge representation formalism and they explained their recommendations by rephrasing the rules involved in the inference chain that led to the solution. However, blanking technology design is a complicated, experiencebased process including many procedures such as blanking, bending and local forming, as shown in Fig. 1. These procedures involve an amount of complicated domain knowledge, and so a ®rst generation expert system is dif®cult to meet the requirements of blanking technology design. Second generation expert systems are distinguished from their counterparts of the ®rst generation in the following ways [1]: (1) they organize their knowledge base in modules and multi-level structures; (2) they employ *
Corresponding author. Present address: Department of Mechanical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794-2300, USA. E-mail address: [email protected] (X. Chi).
hybrid representations by combining multiple representation schemes; (3) they supply sophisticated semi-automated and automated tools to help with knowledge acquisition; (4) they can provide more satisfactory and comprehensive explanations of their actions. As a feature of second generation expert systems, they separate control knowledge from domain-speci®c knowledge and organize the knowledge base in multi-level structures. These advantages of a second generation expert system can provide a mechanism re¯ecting practical design process, and will improve the system's intelligence and automation when applied in blanking die CAD. 2. Traditional knowledge representation formalism Simply put, the phrase ``knowledge representation'' means that knowledge about facts and relations is encoded in a manner that allows computers to recognize it. Knowledge representation is very important in building an expert system because the success of the expert system depends largely on the effectiveness of knowledge representation. Most blanking technology design expert systems employ single knowledge representation, the following being the two most commonly used types [2]: 1. Production-rule format. The format can encode knowledge as follows: IF (prerequisite/condition) THEN (action) The following rule is an example:
0924-0136/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 0 1 3 6 ( 0 1 ) 0 0 8 2 4 - X
96
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
Fig. 1. Blanking technology classification.
IF the hole-to-hole distance has precision requirement THEN holes should be pierced at the same step in order to decrease accumulated dimensional tolerances caused by location errors 2. Frame representation. The basic structure can be described as follows: {
There are two types of knowledge contained in the knowledge base of blanking technology: domain knowledge and control knowledge. Domain knowledge is speci®c to the problem at hand. Control knowledge is more of abstract nature and describes the overall objectives and strategies of how the system is to pursue a goal. In ®rst generation expert systems, no distinction was made between the two types of knowledge and control knowledge would either be absent or implicit and hidden within the domain-speci®c knowledge. Consider the following example: IF there exists two holes whose differences in diameter are great, and the distance between two holes < minimal hole-to-hole distance that will prevent a weak die THEN pierce the large hole first, then the small one There is an important control strategy here which is implicit in the design of the rules. The order of the two conditions of the rule is not arbitrary. The condition ``the distance between two holes < minimal hole-to-hole distance that will prevent a weak die'' should be considered only if ``there exists two holes whose differences in diameter are great''. So, there is an implicit blanking technology hierarchy which determines the rule structure. The control strategy of this rule may be described as follows. Before checking the location, check the diameters. The strategy remains unstated in the knowledge base. However, making such control knowledge explicit can make it more convenient to represent blanking technology and can supply more comprehensive explanations of the system decisions, which
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
97
Fig. 2. Hierarchical knowledge representation illustration.
also attributes to building and maintaining the knowledge base. Take the above rule for example: two conditions locate at different hierarchies. When the top domain knowledge (i.e. there exists two holes whose differences in diameter are great) is activated, the down domain knowledge (the distance between two holes < minimal hole-to-hole distance that will prevent a weak die) is activated through control knowledge; thus the domain knowledge is separated from the control knowledge. The above example is one rule about blanking which is easy to understand. However, the process of blanking is complicated and involves many factors that have to be considered. For example, when piercing holes in the process of strip layout design, many related factors must be taken into account. The related rules are as follows: Rule 1: If the hole-to-hole distance has precision requirement, then holes should be pierced at the same step in order to decrease accumulated dimensional tolerances caused by location errors. Rule 2: If the hole-to-contour distance has precision requirement, then the hole should be pierced at one step before the blanking step as far as possible so that it is approximately close to the blanking step. Rule 3: Pilot holes should be pierced at the ®rst step in order that piloting could work at the following steps. Rule 4: If a hole has high-dimensional precision and contour requirements (especially to thin strip materials), then the hole should be pierced at the back steps in order to avoid the in¯uences of other steps. Rule 5: If a hole interferes with many other holes (i.e. the minimal hole-to-hole distance > permitted minimal value), then the hole should be pierced at the next step.
Rule 6: If there exists two holes whose differences in diameter are great, and the distance between two holes < minimal hole-to-hole distance that will prevent weak die, then pierce the large hole ®rst, then the small one. Rule 7: With the strip material rigidity requirement, pierce the small hole ®rst, then the large one. In the rule-based expert systems, the rules are stored in the knowledge base as text and have no clear and de®nite classi®cation. In this paper, the authors put these rules into hierarchical organizations. Although it has been noted that every person has a different hierarchy of concept which is in¯uenced by one's personal experiences and beliefs, it is still feasible to employ a hierarchical organization of knowledge in the ®eld of blanking technology which has been developed maturely for many years. Fig. 2 gives an illustration of hierarchical knowledge representation. The illustration is a general thought which ignores some details. For example, in Rule 6, more details discussed above here are ignored. 4. Blackboard model supporting hybrid knowledge representation Blanking technology includes many types of knowledge each of which employs different knowledge representation format. Now, it is possible to employ hybrid knowledge representation by applying hierarchical concept to the organization of blanking technology knowledge base and realizing the separation of domain knowledge and control knowledge. This paper gives an object-oriented blackboard model which employs many types of knowledge representation formats
98
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
and forms hybrid knowledge representation under the administration of blackboard. 4.1. O-o technology O-o (object-oriented) technology is an essential ®eld of software in recent years. The main building block of an o-o program is object which simulates real-world concept. O-o technology describes the data structure of objects to build the system, which matches the human's cognitive process. It realizes data abstraction, information hidden and class inheritance between objects. The concept of class re¯ects the human's cognitive process from the general to the speci®c. So, it is feasible to apply o-o technology to the design of expert systems. 4.2. Blackboard model The blackboard model provides a very ¯exible control structure for judging and solving problems. In the blackboard model, information in different processes (or at different levels) is described on the blackboard, then recommending/reference information is output. So, the characteristics of the blackboard is that it is a shared data area, or it is an interactive medium of stored information (data) and
knowledge sources. Blackboard model usually consists of three major components [4]: 1. Knowledge source. Knowledge used to solve problems is divided into many knowledge sources which are stored individually and independently of each other. 2. Blackboard data structure. The blackboard is referred to as a global database that records and organizes intermediate problem-solving data. Knowledge sources produce changes to the blackboard that lead incrementally to a solution to the problem. Communication and interaction among knowledge sources take place solely through changes on the blackboard. 3. Controller. The KSs (knowledge sources) respond opportunistically to changes on the blackboard. The control shells, which lie in KSs, blackboard and isolated modules or the combination of the above three items, monitor the changes on the blackboard and decide which action to take next. A blanking technology design process is characterized by the following requirements: (1) it is a creative process; (2) it contains a variety of information; (3) multiple design results will be given. In order to meet these requirements, expert systems should provide a variety of knowledge representation formats to build KSs base. An object-oriented
Fig. 3. Blackboard model.
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
blackboard model can realize hybrid knowledge representation, such as production rules, frameworks and procedures. According to the characteristics of blanking technology, different types of knowledge are represented in different ways, which are embedded in blackboard model to produce an o-o blackboard model supporting hybrid knowledge representation. In this way, high ef®cient administration of blackboard combined with the advantages of hybrid knowledge representation and o-o technology can be incorporated to the complicated blanking technology expert system design. Fig. 3 shows a blanking technology expert system blackboard model. As shown in Fig. 3, the blackboard is a global database which stores necessary conditional information and solutions recommended by knowledge sources. Interaction among independent KSs must take place through blackboard. When a rule of blanking technology is activated, a message is sent to the blackboard which processes the message and then sends another message to knowledge base. The knowledge source with the highest priority is
99
executed, it will complete one or more of the following procedures: (1) add a hypothesized element to the dynamic blackboard; (2) revise existing elements; (3) produce a link object of some hypothesized element. Then, the control shell in the blackboard will monitor the changed element, activate the correspondent KSs and produce further changes on the blackboard until these changes lead to a satisfactory blanking technology decision. 4.3. An example The processing feature is the main criteria in blanking technology design. Thus, this paper employs hybrid knowledge representation based on hierarchical knowledge representation and takes processing features as objects. In this method, frameworks are used to describe object which includes object name, attributes and operations on object. Production rules and procedures can be combined with frameworks to represent complete and detailed blanking knowledge. Object name represents processing feature,
Fig. 4. An example of the blackboard model supporting hybrid knowledge representation about the strip layout process.
100
X. Chi et al. / Journal of Materials Processing Technology 116 (2001) 95±100
attributes include the super-class of the object, knowledge type and object class, method means dynamic actions of objects and procedure knowledge processing. Blackboard is a dynamic monitor which processes all types of knowledge and at the same time, its functions include receiving and sending messages and searching matching rules, etc. Fig. 4 gives an example of a blackboard model supporting hybrid knowledge representation about the strip layout process. Operation sequence arrangement is one of the sub-classes of strip layout, being derived from class strip layout. The knowledge is stored at different levels of knowledge sources and is activated by the blackboard event driven by messages [5,6]. 5. Conclusions An expert system for blanking technology is signi®cant in improving die CAD intelligence. However, blanking technology design is a complicated and ¯exible process which makes it challenging to build an expert system. In this paper, the authors describe the characteristics of a second generation expert system, and give an o-o blackboard
supporting hybrid knowledge representation. In addition, how knowledge and experience are represented in knowledge base is also discussed. Since there are many functions for further development, it is possible to enhance the research in the future.
References [1] C. Nikolopoulos, Expert System, Marcel Dekker, New York, 1997. [2] X. Xiangzhi, et al., Blanking Technology and Die CAD, National Defence Industrial Press, Beijing, 1996 (in Chinese). [3] M. Kamel, Y. Quintana, Generation of Concept Hierarchies for Engineering Knowledge-based Systems, Application of Artificial Intelligence in Engineering VII, Computational Mechanics Publications, 1992, pp. 107±124. [4] H. Bo, Z.K. Ni, Structural design of an intelligent decision system generator based on blackboard model, Comput. Res. Dev. 34 (5) (1997) 382±386 (in Chinese). [5] B.N.K. Ann, C.C. Kai, A knowledge-based system for strip layout design, Comput. Ind. 25 (1994) 31±44. [6] J.C. Choi, B.M. Kim, Y. Cho, C. Kim, J.H. Kim, An integrated CAD system for the blanking of irregular-shaped sheet metal products, J. Mater. Process. Technol. 83 (1998) 84±97.