Intelligent approaches to tolerance allocation and manufacturing operations selection in process planning

In the modern manufacturing environment, alternative sets of manufacturing operations can normally be generated for machining one feature of a part. Each set of manufacturing operations results in a specific manufacturing cost in terms of the allocated tolerances, and requires a specific set of manufacturing resources, such as machines, fixtures/jigs and cutting tools. In this paper, the problems of allocating tolerances to the manufacturing operations and selecting exactly one representative from the alternative sets of manufacturing operations for machining one feature of the part are formulated. The purpose is to minimize, for all the features to be machined, the sum of the costs of the selected sets of manufacturing operations and the dissimilarities in their manufacturing resource requirements. The techniques of the genetic algorithm and the Hopfield neural network are adopted as possible approaches to solve these problems. The genetic algorithm is utilized to generate the optimal tolerance for each of the manufacturing operations, and the Hopfield neural network is adopted to solve the manufacturing operations selection problem. An illustrative example is given to demonstrate the efficiency of the proposed approaches. Indeed, the proposed approaches show the potential of working towards the optimal solutions to the tolerance allocation problem and the manufacturing operations selection problem in process planning.