Selection of Best Alternative Process Plan in Automated Manufacturing Environment: An Approach Based on Particle Swarm Optimization

摘要

The burgeoning scenario of Flexible Automation and Intelligent Manufacturing recognizes the selection of optimal process plan from the available alternatives as most complex and crucial task to be accomplished. Process planning can be outlined as systematic determination of processing steps to transform a raw material to its finished product. The complexity of the problem can be gauged from the exponential growth of the number of alternative solutions with the increase in number of parts and their alternative process plans. This paper attempts the complex Process Plan Selection (PPS) problem using an Intelligent Particle Swarm Optimization algorithm with modified concept of Local Repeller (IPSO-LR). The paper formulates the PPS problem in a more justifiable way by the incorporation of a new parameter termed as Similarity Attribute (λ) that keeps the track of similarity among part types to be manufactured. The algorithm emulates the behavior of particles in a swarm and explores the search area by interacting with neighbors and utilizes the successes of other particles with regard to reaching towards optima. Robustness and efficacy of the proposed strategy is established by solving the problem of real dimensions and comparing the results with the established solution methodologies in process planning field.