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Abstract It is widely accepted that stamping process planning for the strip layout is a key task in progressive die design. However, stamping process planning is more of an art rather than a science. This is in spite of recent advances in the field of artificial intelligence, which have achieved a lot of success in incorporating built-in intelligence and applying perse knowledge to solving this kind of problem. The main difficulty is that existing knowledge-based expert systems for stamping process planning lack a proper architecture for organizing heterogeneous knowledge sources (KSs) in a cooperative decision making environment. This paper presents a knowledge-based blackboard framework for stamping process planning. The proposed approach speeds up the progressive die design process by automating the strip layout design. An example is included to show the effectiveness of the proposed approach.41382
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Keywords Blackboard framework• Graph-based Knowledge-based • Object-oriented • Progressive die design •Stamping process planning
1 Introduction
Progressive dies for producing sheet metal parts in mass production have been widely applied in various industries such as aerospace, electronics, machine tools, automobiles, and refrigeration. These dies can perform piercing, notching, cut-off, blanking, lancing, bending, shaving, drawing, embossing, coining, trimming, and other miscellaneous forming operations at a single setup. Hence, a progressive die is generally very complex.
Stamping process planning and die structure design are difficult and demanding tasks.
Stamping process planning starts with an unfolding of a model of stamped metal part to produce a flat pattern, followed by nesting the pattern to produce a blank layout. Next, stamping operations are planned and operations are assigned to die stations. The resulting plan is typically represented as a strip layout, which guides the subsequent die structure design. The productivity, accuracy, cost, and quality of a progressive die mainly depends on the strip layout, and hence a stamping process. However, stamping process planning still remains more of an art rather than a science. Historically, this activity is mainly carried out manually, based on designers’ trial-and-error experience, skill and knowledge.
Recent advances in the field of artificial intelligence (AI) have given rise to the possibility to construct AI-based systems that incorporate built-in intelligence and apply perse knowledge to solving progressive die design problems, including strip layout design automation. The perse knowledge sources (KSs) related to stamping process planning include unfolding knowledge to produce a flat pattern, nesting knowledge to produce a blank layout, mapping knowledge to transform stamping features into stamping operations, and staging knowledge to sequence the stamping operations. A discussion of some knowledge-based progressive die design work related to our study can be found in Sect. 2. However, the existing work is based on the conventional architecture of knowledge-based expert systems, which are incapable of managing heterogeneous KSs effectively. These limit both their practicability and scalability.
To address the above issue, it is necessary to provide a cooperative problem solving strategy that can foster communication between perse KSs, and accommodate different knowledge representation schemes within an integrated framework. Hence, a knowledge-based blackboard framework consisting of a blackboard control system and a few independently executing KSs have been developed. This framework provides a cooperative decision making environment and facilitates a hybrid knowledge representation scheme, including procedures, production rules, and object-oriented representations. A prototype system has been implemented using the object oriented expert system shell CLIPS (C Language Integrated Production System) [1], which is interfaced with a parametric- and feature-based CAD system, Solid Edge through C++. An example is provided to demonstrate our approach and to show its effectiveness in stamping process planning.