Particle swarm optimization in multi-stage operations for operation sequence and DT allocation

Abstract

Improved operation sequence and economic tolerance allocation directly influence product quality and manufacturing costs. The purpose of this study is to generate the optimal operation sequence and allocate economic tolerances to cutting surfaces to achieve the specified quality and minimize the manufacturing costs. Because this type of problem is a multi-objective optimization problem subject to various constraints, it is defined as an NP-hard problem. A three-step procedure is used to solve the problem. First, a mathematical model is developed to define the relationships between manufacturing costs and tolerances. Second, an artificial neural network (ANN) is applied to obtain the best fitting cost-tolerance function. Finally, the formulated mathematical models are solved by using particle swarm optimization (PSO) in order to determine the optimal operation sequence. In addition, both the effectiveness and efficiency of the proposed methodologies are tested and verified for a given workpiece that needs multi-stage operations. The key contributions of this study are the generation of the optimal operation sequence and the effective allocation of the optimal dimensional tolerance (DT) using an advanced computational intelligence algorithm with consideration for multi-stage operations.