Neural networks applications in parameter setting of tube hydroforming and metal cutting processes

The objective of this research is to test and conclude about the efficiency of ANNs for optimization of manufacturing processes. For this purpose, ANN based methods are proposed to deal with two different manufacturing processes. The first problem is the tube hydroforming process with two conflicting objectives. A two-stage neural network approach and a hybridization of ANNs with genetic algorithm are proposed for the solution of the problem. Simulation outcomes of the proposed approaches are compared with Taguchi approach. The results show that ANNs need to be integrated by other solution techniques since combining neural networks with genetic algorithm provide the best process performance for tube hydroforming process under consideration. The second problem is the process parameters optimization of a metal cutting process with unit cost minimization. The original integer programming model of the problem given in the literature is used to construct the energy function by using penalty approach. For this problem, a maximum and continuous neural network interacting with each other are proposed. The results are compared with optimum results of dynamic programming, integer programming and non-linear programming. The results show that neural networks are an effective alternative to operation research techniques and combining Hopfield-type networks with penalty approach gives the advantage of obtaining optimal solution in an extremely large solution space within a reasonable computation time.The contribution of this thesis is two fold. One is to the manufacturing process literature as this study is the first attempt to solve the parameter optimization problems of the manufacturing processes under consideration. This thesis also makes contribution to ANN literature as combining ANNS with different techniques for optimization of manufacturing processes.