科研之友
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摘要
Process planning is a function that establishes the technological requirements necessary to convert a part from its raw material to the finished form. Generally, the result of process planning is delivered to the workshop to guide the manufacturing process in the form of process plan. However, a part always has multi alternative process plans for the processing means and techniques are not unique, therefore, optimization and selection of process plans is an important task of flexible process planning. In this paper, the flexibility of process planning and the AND/OR network adopted to represent the flexibility of process plans were described, and a mathematical model for the optimization of flexible process planning based on the AND/OR network was established. On this basis, a new heuristic method, called cross-entropy (CE) approach, was proposed to optimize flexible process planning. In order to facilitate the implementation of the CE-based approach, the new sample representation and probability distribution parameter were introduced; meanwhile, the new sample generation mechanism was presented and the updating expression of probability distribution parameter was deduced. Case studies, used for comparing this approach with genetic algorithm (GA) and genetic programming (GP)-based approach, were discussed to indicate the performance and adaptability of the proposed CE-based approach in terms of the solution quality and computational efficiency of the algorithm. The results show that the CE-based approach is effective for the optimization research of flexible process planning.
