Taguchi based Utility and Grey Relational Approaches to optimize Bi-Objective Machining of AISI 202 Stainless Steel
K.Krishna Mohan Reddy1, K.Srinivasulu Reddy2, M.Gopi Krishna3

1K. Krishna Mohan Reddy, Research Scholar, Department of Mechanical Engineering, Acharya Nagarjuna University, Guntur (A.P), India.
2K. Srinivasulu Reddy, Department of Mechanical Engineering, Sreenidhi Institute of Science and Technology, Hyderabad (Telangana), India.
3M. Gopi Krishna, Department of Mechanical Engineering, Acharya Nagarjuna University, Guntur (A.P), India.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1641-1645 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7632068519/19©BEIESP
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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: To optimize single response problems traditional Taguchi approach is widely used. Performance evaluation of the manufacturing process is often determined by several quality characteristics. Under such circumstances, multi-characteristics response optimization is the solution to optimize multi-objective quality characteristics. In the present work, bi-objective characteristics response optimization model based on Taguchi based Grey relational analysis and Utility approach is used to optimize process parameters, cutting speed, depth of cut, feed and nose radius on two different performance characteristics namely surface roughness (Ra) and material removal rate (MRR) during dry turning of austenitic stainless steel AISI 202 with cemented carbide tipped tool. Both approaches are analysed using Taguchi’s L8 orthogonal array (OA) and found that both approaches predicted the same experimental settings, higher levels of cutting speed, depth of cut, nose radius and lower level of feed are critical to achieve low surface roughness and high material removal rate simultaneously.
Keywords: Utility Approach, Grey Relational Analysis

Scope of the Article: Machine Learning