Optimization of Fixture Layout and Artificial Neural Network (ANN) Weights of ANN-Finite Element Analysis Based Fixture Layout Model Using Genetic Algorithm

Abstract

Workpiece elastic deformation in machine manufacturing may cause dimensional errors, which in turn affects the accuracy of the machined parts. Fixturing elements like locators and clamps are used to locate a workpiece with respect to the cutting tool in a given orientation such that the errors caused by workpiece elastic deformation are reduced. The optimization of locator and clamp positions is crucial in minimizing the dimensional errors in machining. In this research paper, a slot milling operation on a rectangular workpiece is considered for which the fixture layout is optimized using a hybrid system of artificial neural network (ANN) and genetic algorithm (GA). The workpiece elastic deformation for different sets of fixture layouts is calculated using finite element method (FEM) and training of ANN is done with the FEM results to develop a numerical model. To enhance the accuracy of learning in lesser time, the weights are optimized for the network using GA before the training phase. The trained ANN recognizes a pattern between the position of fixturing elements and the workpiece elastic deformation. Using the recognized pattern, GA determines the optimal position of locators and clamps to minimize the workpiece elastic deformation and thereby the dimensional errors.