MODELING AND OPTIMIZATION OF END MILLING MACHINING PROCESS

Wds.Milton.Ponnala, K .L .N. Murthy

Abstract: Generally in any manufacturing industry, a human process planner selects the machining parameters based on his expertise or from data handbooks; they do not represent the optimal values. The optimization process involves the optimal selection of machining parameters such as cutting speed, feed and depth of cut, subjected to practical constraints of surface finish, tool wear, dimensional accuracy and machine tool capabilities. Several researches have used different techniques in literature to optimize machining process by considering a machining problem as single objective optimization problem. However a machining problem should be treated as a multi objective problem as it involves two conflicting objectives: machining time and production cost. In such problems there cannot be single optimal solution. To get all optimal solutions, a multi objective optimization method called Non-dominated Sorting Genetic Algorithm (NSGA) is proposed in the project work. In the first part of the present work, mathematical relationships between input and output parameters have been developed by Response Surface Methodology (RSM). Consequently there have been solved to get the optimal values. The general second order composite rotatable design is used in planning and modeling the experiments. The experiments were conducted on the general purpose milling machine using a 60mm*60mm*40mm block (AISI 1040steel).In the second part a multi optimization algorithm called non dominated sorting genetic algorithm was used to retrieve all set of optimum values. In NASA, the non-dominated sorting procedure is used to bring forth the good points of correct population and stable subpopulations of good points are maintained by Niche method. The present work enables the industries to have the optimum values of the milling process variables and conducting the process can be automated based on optimal values.

Keywords: AISI 1040steel, Non-dominated Sorting Genetic Algorithm (NSGA), Optimization, Response Surface Methodology (RSM), and Multi Optimization Algorithm etc…

DOI: https://doi.org/10.15623/ijret.2012.0103041