Prediction of Machining Fixture Layout through FEM and ANN and Comparison of Optimal Fixture Layouts of GA and ACA

Abstract

In machine manufacturing, workholding plays a vital role in immobilizing the workpiece to provide maximum accuracy. The workpiece should be securely located and held against the cutting tool to reduce workpiece deformation thereby reducing dimensional and form errors. Fixtures are workholding devices used in machining and assembly for holding, supporting and locating a workpiece in a given orientation. Fixturing elements such as locators and clamps are used to deprive the workpiece of all degrees of freedom so that it is constrained at all times to prevent any movement. The optimal positioning of locators and clamps highly influences the workpiece deformation and thereby the machining errors. A two-dimensional workpiece geometry involving a slot milling operation has been considered for the research work for which the workpiece deformation for different fixture layouts is determined using finite element method (FEM). The different layouts and the corresponding workpiece deformations from the simulation results are made available as the database for artificial neural network (ANN) to develop a numerical model that recognizes a pattern between the position of fixturing elements and the workpiece deformations. Using the recognized pattern from ANN, genetic algorithm (GA) based continuous optimization method and ant colony algorithm (ACA) based continuous optimization method have been used to determine the optimal position of locators and clamps to minimize the workpiece deformation and thereby the dimensional errors