An integrated stochastic model to optimize the machining condition and tool maintenance policy in the multi-pass and multi-stage machining

Developing the mathematical optimization models for multi-pass and multi-stage machining processes, due to their mathematical and implicational complexity is important. Despite extensive research on the optimization of machining processes by considering different assumptions, few studies have been conducted on the processes in which the tool passes over the workpiece surface several times at different machining stages. Another challenge associated with optimizing the machining processes is to present a flexible and efficient policy for tools replacement and inspection. In this research, a nonlinear mathematical model is proposed to optimize the machining conditions and the tool replacement time based on a hybrid policy in which tool condition monitoring could be discretely and continuously. Considering these assumptions, when the tool life in the various stages of machining follows the Weibull Distribution, it brings the problem closer to real conditions. The objective of the developed model is to optimize the total machining costs and quality of the manufactured products in multi-pass and multi-stage machining. A particle swarm optimization (PSO) algorithm is developed to solve the mathematical optimization model presented in this study, then the PSO results are evaluated using the sequential quadratic programming (SQP) algorithm results.