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Title: CAD/CAM --on the integration of CAPP/CACE for the generation of least-cost machining-process plan for multi-feature work pieces

Book ·
OSTI ID:6506523

Planning has always been, and perhaps will always be, a decisive human trait. Recently, substantial accomplishments have been reported in making computers emulate this fundamental human characteristic. As a consequence, much technical improvement has also occurred in the area of Computer Aided Design (CAD), Computer Aided Process Planning (CAPP), Computer Aided Cost Estimating (CACE) and Computer Aided Manufacturing (CAM). There have been significant results in applying Artificial Intelligence (AI) techniques in automated process planning. The rule which human experts utilize to make decisions that involve the complex and nonquantifiable factors of machining are stored and used by an efficient CAPP system to automatically plan for the machining of parts. Despite these recent accomplishments, all CAPP systems developed to date are, at best, able to generate a low-cost machining sequence for only one form feature at a time. In this research, a model is developed for generating a process plan resulting in the least cost of machining of multi-feature work-pieces. A prototype demonstration system, Planner for Cost Control (PCC) is then presented and illustrated within the proposed framework. The decision rules utilized by human planners do not involve the detailed cost implications of decisions involved in all feasible processes associated with the sequencing of machining operations. Therefore, a knowledge-based CAPP system is developed in order to generate only feasible alternative machining operations. Detailed and coat-optimal cutting parameters are then quickly calculated by a CACE system. Further, these results are manipulated by an algorithm that accounts for tool movement and tool change costs and selects a low coat sequence of machining operations for the entire part.

Research Organization:
University of Southern California, Los Angeles, CA (USA)
OSTI ID:
6506523
Resource Relation:
Other Information: Thesis (Ph. D.)
Country of Publication:
United States
Language:
English