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Article type: Research Article
Authors: Chakraborty, S.a; * | Jana, T.K.b | Paul, S.c
Affiliations: [a] Department of Mechanical Engineering, Bengal Institute of Technology, Santiniketan, India | [b] School of Engineering, Haldia Institute of Technology, Haldia, India | [c] Department of Information Technology, Haldia Institute of Technology, Haldia, India
Correspondence: [*] Corresponding author: S. Chakraborty, Department of Mechanical Engineering, Bengal Institute of Technology, Bolpur, Santiniketan 731236, India. E-mail: souvickchakraborty61@gmail.com.
Abstract: The paper optimizes several performance attributes of metal cutting operation using Multi Criteria Decision Making (MCDM) technique by employing a single set of process variables. Although MCDM technique is widely used for ranking several alternatives based on certain criteria, its applicability is not hitherto investigated in process parameter optimization. The motivation of the work is to establish the technique as a potential approach for optimization of metal cutting process. Four responses namely metal removal rate (MRR), surface roughness, tool life, and cutting force are considered for optimization in terms of cutting speed, feed and depth of cut. Two different methods under MCDM: the TOPSIS and the MOORA are adopted. The best parametric combination obtained by two methods is found to be identical and a healthy correlation also exists between two different rank sets as reflected through the Spearman’s coefficient. A sensitivity analysis is carried out to establish the robustness of the result. The result of MCDM is verified by Genetic Algorithm. It is concluded that the MCDM technique can be applied successfully in process optimization and the method is efficient in terms of computational simplicity, time, and cost as compared to other popular methods and therefore amenable for shop-floor implementation.
Keywords: Design of experiments, entropy, metal cutting, MCDM, MOORA and TOPSIS optimization, closeness coefficient, composite score, Spearman’s coefficient
DOI: 10.3233/IDT-190356
Journal: Intelligent Decision Technologies, vol. 13, no. 1, pp. 101-115, 2019
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