Multi Objective Optimization of Turning Process using Grey Relational Analysis and Simulated Annealing Algorithm

Multi objective optimizing of machining processes is used to simultaneously achieve several goals such as increased product quality, reduced production time and improved production efficiency. This article presents an approach that combines grey relational analysis and regression modeling to convert the values of multi responses obtained from Taguchi method design of experiments into a multi objective model. The proposed approach is implemented on turning process of St 50.2 Steel. After model development, Analysis of Variance (ANOVA) is performed to determine the adequacy of the proposed model. The developed multi objective model is then optimized by simulated annealing algorithm (SA) in order to determine the best set of parameter values. This study illustrates that regression analysis can be used for high precision modeling and estimation of process variables. Introduction One of the most important methods in production of metal parts is machining. Turning is the most widely used machining processes that may result in high precision and quality and increased productivity. However, the quality of final product and its production cost heavily depend of process parameters values. Single purpose control and process optimization can't satisfy economic demands such as reducing time and costs with maintaining quality at the same time. This is because quality improvement usually increases production costs and thus productivity decreases. The use of traditional optimization methods such as differential measures and enumeration of all possible solutions is not very efficient and accurate. Use of metaheuristic algorithms in such incidents can improve speed and accuracy of the computations [1]. In many industrial applications related to the turning process, control and optimization of surface quality (SR) and material removal rate (MRR) are the most important performance measures and considered as the main responses [1-3]. Simulated annealing (SA) algorithm is one of efficient innovative optimization algorithms in solving optimization problems. This algorithm was introduced in 1982 by “Kirkpatrik”, “Gelatt” and “Vecchi” [4]. Adaptability and ease of programming over the optimization problems and tolerability of feasible non improving solutions are the most important features of this algorithm. In this article, a multi objective optimization model for CNC turning of St 50.2 Steel, has been developed and solved using SA algorithm. The model has been converted to a weighted multiple performance characteristics Grey Analysis method. The multiple performance characteristics include material removal rate (MRR) and surface roughness (SR). Three important machining parameters; namely cutting speed, feed rate and depth of cut are considered as the input process parameters. The analysis of variance (ANOVA) is also conducted to estimate the relative effect of each process parameters. Applied Mechanics and Materials Vols. 110-116 (2012) pp 2926-2932 Online available since 2011/Oct/24 at www.scientific.net © (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.110-116.2926 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 217.219.244.217-10/12/11,17:47:42) Design and experimental conditions Many factors affect the material removal rate and surface roughness in turning process. The important machining parameters include feed rate (F), depth of cut (D) and cutting speed (S). Fig. 1 schematically illustrates the turning mechanism with respect to its process variables. Figure 1. Machining parameters in turning process Basically material removal rate is related to these three machining parameters. Material removal rates are determined by the following equation [1]. M = S. F. D (1) In this equation, S is cutting speed (m.min-1), F is feed rate (mm.rev-1) and D is the depth of cut (mm). Another measure of machining is surface quality, usually measured in terms of surface roughness. Surface roughness is one of the most important parameter of turned parts that affects such characteristics as fatigue resistance, cleaning ability, assembly tolerances and friction coefficients [6]. Cutting speed, feed rate and depth of cut have significant effect on average surface roughness (Ra) [7]. Nevertheless, in most cases MRR and SR are acting opposite. Improving SR may cause the MRR to decrease and vice versa. Therefore, these two measures should be considered simultaneously. In this paper based on the experimental data obtained by Taguchi design of experiments matrix, mathematical models are developed to relate input parameters to output response characteristics. The 16 sets of data used for modeling, are obtained using L16 orthogonal array matrix in Taguchi Design of Experiments [5]. The tests were performed under the following conditions: Machine tool: Tezsan-Oncu 260-330.600-C CNC Lathe Cutting tools: SECO inserts (SECO-DCMT11T304-F1) with the tool nose radius of 0.4mm Work parts: St 50.2 Steel with the length of 100mm and 25mm in diameter Surface finish was measured using an automatic digital Hommel Surface Roughness Tester (T20). Table I, lists the ranges of machining parameters. Each parameter is considered to have 4 levels. TABLE I. Experimental Machining Parameters Cutting parameters Range of change Cutting speed (S) 100 250 (m.min -1 ) Feed rate (F) 0.1 0.3 (mm.rev -1 ) Depth of Cut (D) 0.5 – 2 (mm) In Table II, the output results for all 16 test runs are given. Applied Mechanics and Materials Vols. 110-116 2927