Application of RSM, F-Regression and GA for evaluating of effective factors in vehicle brake drum assembling process

The present study aims at optimizing the vehicle brake drum assembling process. Considering the crucial role of the Axle unit, especially its vehicle brake drum which is related to the safety of the passengers, the study of the producing and assembling processes and conducting the quality control experiments during these stages is of great importance. With regard to the great significance of three main factors, namely, seal-oil spindle diameter, seal-oil internal diameter, and nut lock torque as independent variables, the present research attempts to optimize the rotatory torque of the automobile brake drum getting help from the discussions in the RSM and the unsteady of the automobile brake drum getting help from fuzzy regression using least absolute deviation estimators. Finally optimal solution perused by nonlinear programming model and Genetic Algorithm using one of multi-objective existing methods (LP-metric). Comparing the two optimization methods is shown that the GA technique has better performance rather than nonlinear programming model.