Comparison of Optimum Cutting Parameters for AISI1042 in Turning Operation by Genetic Algorithm and Particle Swarm Optimization

Abstract:

In metal cutting surface finish is a crucial output parameter in determining the quality of the product. Good surface finish not only assures quality, but also reduces manufacturing cost. Surface finish is an important parameter in terms of tolerances, it reduces assembly time and avoids the need for secondary operation, thus reduces operation time and leads to overall cost reduction. It is very important to select optimum parameters in metal operations. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The main aim of the present work is to build a model to solve real world optimization problems in manufacturing processes.The selection of optimal cutting parameters are speed, feed and depth of cut. are important for all machining process. Experiments have been designed using Taguchi technique, dry and single pass turning of AISI No. 1042 (EN-41B) steel with cermet insert tool performed on PSG A141 lathe. By using signal to noise (S/N) ratio and Analysis of variance (ANOVA) are performed to find the optimum level and percentage of contribution of each parameter. A mathematical model is developed using regression analysis for surface roughness and the model is validated.Moreover, the proposed algorithm, namely GA and PSO were utilized to optimize the output parameter R_a in terms of cutting speed, feed and depth of cut by using MATLAB.

• Periodical: Applied Mechanics and Materials (Volumes 813-814)
• Pages: 285-292
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.813-814.285
• Online since: November 2015
• Authors: A. Hemantha Kumar*, G. Subba Rao, T. Rajmohan
• Keywords: ANNOVA, Design of Experiments (DOE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Surface Roughness (Ra), Turning

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