Optimization for Machining Parameters in Turning of AISI 52100 Steel for Manufacturing Services Genetic Algorithm Method
A. V. Pradeep1, L. V. Suryam2, S. V. Satya Prasad3, K. Vahini4, P. Prasanna Kumari5
1A. V. Pradeep, Department of Mechanical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
2L. V. Suryam, Department of Mechanical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
3S. V. Satya Prasad, Department of Mechanical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
4K.V ahini, Department of Mechanical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
5P. Prasanna Kumari, Department of Mechanical Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India.
Manuscript received on December 18, 2019. | Revised Manuscript received on December 26, 2019. | Manuscript published on January 10, 2020. | PP: 1297-1304 | Volume-9 Issue-3, January 2020. | Retrieval Number: B6624129219/2020©BEIESP | DOI: 10.35940/ijitee.B6624.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Because of its high wear and corrosion resistance, AISI 52100 steel is mostly used in automobile and aerospace applications. It is a high strength chromium steel with hardness usually greater than 45HRC.The prime objective of this study is to identify the influential machining parameters affecting metal removal rate (MRR) and cutting forces. During turning of AISI52100 steels, a central composite design (CCD) through response surface methodology (RSM) was employed to generate a model for predicting the MRR and cutting forces. The significant parameters and their contribution percentage were identified using analysis of variance (ANOVA). Furthermore, the affect of machining parameters in the MRR and cutting forces were examined and validated for identifying the accuracy of the predicted model. To conclude, the optimum values of MRR and cutting force along with the optimum parameters were ascertained using genetic algorithm (GA).
Keywords: Cutting force, MRR, RSM, GA, AISI52100
Scope of the Article: Algorithm Engineering