Improvement of Computational Efficiency in Flexible Computer-Aided Process Planning

Abstract

Process planning plays an important role as a bridge between product design and manufacturing. Computer-aided process planning (CAPP) has been a topic of discussion in this half century. The recent diversification in customers’ needs has been driving the development of agile manufacturing that can adapt to different manufacturing situations. CAPP should also be discussed from this point of view and, to this end, a set of flexible process planning methods have been proposed. Unlike conventional CAPP methods, these methods first generate all the feasible process plans. These are then evaluated, and then an optimal plan is selected. Therefore, it is possible to quickly provide an optimal new plan in the event of a change in the situation, by re-evaluating the plans against the new situation. However, these methods generally involve a large computational load, since the full search approach is taken to select an optimal plan. This study set out to reduce the computational load by formulating the selection process as a 0-1 integer programming problem that can now be solved thanks to recent developments in computer technology and solvers. Case studies have proven the efficacy of this method.