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2 Related works
Research in the computer-aided stamping process planning has been widely reported since the 1970s. The advantages of automated process planning are productivity improvements, cost reductions, and design automation.
From the mid1970s to mid1980s, the first generation of CAD/CAM systems for progressive die design were developed [2–5], though few of them are based on AI techniques. These early systems are characterized by basic computer graphics facilities, standardization of die components, and standardization of design procedures. They reduced design and drafting lead time. However, as these systems represent design know-how in the form of conventional procedural programming languages, only generation of the die part list and drafting of the assembly and part drawings are executed using computers. The designer still needs to decide most of the important decisions interactively, including strip and die layouts.
Since the late 1980s, significant efforts have been made by worldwide researchers to integrate a wide variety of AI and traditional CAD approaches to develop dedicated progressive die design automation systems, including strip layout design automation.
Knowledge-based approach is a popular AI technique that has been used in intelligent stamping process planning and die design system. For example, researchers at the University of Massachusetts, USA have described a knowledge-based system for design of progressive stamping dies for a simple hinge part [6]. The system generates the flat pattern geometry and develops a strip layout automatically. Researchers at the National University of Singapore have been developing an intelligent progressive die (IPD) design system since the late 1980s. They used feature modeling and rule-based approach to realize automatic punch shape selection, strip layout development, and 3-D die configuration [7, 8]. Based on a feature-relationship tree that describes the stamped metal part and its topological information, model-based reasoning and spatial reasoning techniques have been employed to reason out certain stamping processes and guide the overall planning process to develop the strip layout automatically. Researchers at the Indian Institute of Technology have developed a computer-aided die design system, CADDS, for sheet-metal blanks [9], based on heuristic rule-based reasoning and parametric programming techniques. The greatest advantage achieved by the system is the rapid generation of the most efficient strip layouts. Researchers at the University of Liverpool have worked on design automation for progressive piercing and blanking dies [10, 11]. Their work is based on applying a coding technique to characterize the stamped part geometric features, which is subsequently used to generate the type and layout of the die punches, and then develop the strip layout automatically. Researchers at Huazhong University of Science and Technology, China, have developed an intelligent progressive die design system, HPRODIE [12]. With feature mapping, rule-based reasoning and case-based reasoning techniques, most of the design processes including strip layout design can be carried out automatically. Researchers at Pusan National University, Korea, have developed a compact computer-aided process planning (CAPP) system for progressive die design [13]. Based on production rules, the work is capable of carrying out an intelligent stamping process planning work with automatic development of blank layout, strip layout and die layout.
Though knowledge-based systems have achieved a lot of success in stamping process planning, most of the progressive die design automation prototypes reviewed above are rather restricted to specific application domains, or still need considerable interactive input from experienced designers to develop strip layouts. This is because they still inherit the disadvantages of the conventional architecture of knowledge-based expert systems, which are incapable of managing heterogeneous KSs effectively.
Researchers at the National Taiwan Institute of Technology have adopted various AI techniques including fuzzy reasoning, pattern recognition, rule-based reasoning, back-propagation neural network, genetic algorithms and Petri nets for the stamping process planning and design of progressive shearing cut and bending dies [14–16]. However their work lacks an explicit and consistent model to integrate these AI techniques into a comprehensive design environment.