Multiresponse optimization of end milling process parameters on ZE41A Mg alloy using Taguchi and TOPSIS approach. (2022)

• Record Type: Journal Article
• Authors: Kumar, Rajender; Katyal, Puneet; Kumar, Kamal; Singh, Vijender
• Is Part Of: Materials today. Volume 56:Part 5(2022)
• Page Start: 2497
• Page End: 2504
• Publication Date: 2022
• Subjects: Mg-alloy -- End-milling -- MRR -- Thermal stress -- Taguchi -- TOPSIS; CS cutting speed -- MRR Material removal rate -- EDM Electrical discharge machining -- FR feed rate -- RPM revolution per minute -- ECDM Electrochemical discharge machining -- DoC depth of cut -- ANOVA Analysis of variance -- PSO particle swarm optimization -- TS Thermal Stress -- SAW Simple Additive Weighting -- CIM Computer-integrated manufacturing -- Pi preference value -- GRA Grey relational analysis -- TOPSIS Technique for order performance by similarity to ideal solution; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.08.271
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The effects of CNC end-milling process parameters on the machinability of Mg -alloy are discussed in the present investigation. The optimization of the process parameters namely cutting speed, feed rate and depth of cut has been carried out for material removal rate (MRR) and thermal stresses (TS) using Taguchi design of experiment and TOPSIS approach. The milling parameters have shown significant role towards the machining attributes. Analysis of variance (ANOVA) used to evaluate and analyse the relative importance of milling process parameters. For single response optimization, the most significant factor for MRR was feed rate and for thermal stress TS, it was cutting speed. However, feed rate has the greatest impact on preference value ( PI ), accounting for 71.85% of contribution to multi-response optimization. The recommended milling process parameters have determined to be optimum at cutting speed: 1200 RPM, feed rate: 100 mm/min and depth of cut: 1.25 mm by using the multi-response optimization technique.