Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

Abstract

One of the main objectives of computer-aided process planning is to determine the optimum machining sequences and setups. Among the different methods to implement this task, it can be named the constrained optimization algorithms. The immediate drawback of these algorithms is usually a large space needed to be searched for the solution. This can easily hinder the convergence of the solution and increase the possibility of getting trapped in the local minima. A novel approach has been developed in this work with the objective of reducing the search space. It is based on consolidating the decisive factors influencing the consecutive features. This helps prevent creation of sequences which need the change of setup, machine tool, and cutting tool. The proposed method has been applied to three different optimization methods, including genetic, particle swarm, and simulated annealing algorithms. It is shown that these algorithms with reduced search spaces outperform those reported in the literature, with respect to the convergence rate. The best results are found in the genetic algorithm from the viewpoint of the obtained solution and the convergence rate. The worst results belong to the particle swarm algorithm in connection with the strategy of generating new solutions.