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As a result of slicing a 3D part into 2D pockets for generating tool paths, pockets are becoming more commonly used. When a pocket is machined, the question of which of the many feasible tools for cutting the pocket is best always arises. Will cutting be more efficient if multitools are used? Will the algorithm to choose the optimal tools be timesaving enough? This paper presents a method in which an upper bound O(N) is used to choose the optimal cutting tool combination, where N is the number of feasible tools available. The proposed method is quick and sufficient for computer-aided process planning (CAPP) and computer-aided manufacturing (CAM) environments.

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This work automates process planning and die design in automotive panel production using a novel knowledge-based engineering (KBE) methodology. Automotive panels are more complicated than common stamped parts because automotive panels are composed of groups of freeform surfaces. Stamping process planning identifies and sequences the necessary operations, finally producing the appropriate press dies. Case-based reasoning (CBR) is integrated into ordinary process planning and die design processes to generate a hybrid KBE system. Utilizing the CBR methodology to plan stamping process and design stamping dies for automotive panels reuses existing designs to develop new designs. In the proposed flexible system, process-planning and die-design functions can adapt existing designs or generate new designs based on stamping knowledge. Tacit knowledge of stamping parts is preserved and automotive panel manufacturing is accelerated by design automation when using the proposed KBE system. 76 engineering applications. Wang and Bourne [20] identified important features for the sheet-metal bending process. The application of features and potential problems associated with feature interaction were also discussed. Tang et al. [18] proposed an intelligent feature-based design for a stamping system to evaluate stamping ability. Smith et al.[17] devised a relational system that efficiently integrates feature-based process planning and expert system strategies for sheet-metal parts. However, successful extraction of stamping features during the feature recognition phase is difficult with feature-based techniques, especially when features intersect.An increasing number of advanced technologies, including knowledge-based engineering (KBE), are adopted to support various aspects of the product life-cycle [14][15]. Notably, KBE is a novel artificial intelligence methodology for solving engineering problems. Using intelligent methods for process planning and die design enhances design quality and the ability for innovative design. Based on the KBE technique, engineering tasks are carried out using knowledge not immediately accessible. Therefore, utilizing the KBE methodology, in which experience and knowledge used in current designs improves production efficiency of panel stamping [2].