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Dry Machining Performance Studies on TiAlSiN Coated Inserts in Turning of AISI 420 Martensitic Stainless Steel and Multi-Criteria Decision Making Using Taguchi – DEAR Approach

Publication Type : Journal Article

Source : Silicon, pp.1-14, 2021

Url : https://link.springer.com/article/10.1007/s12633-021-01202-4

Campus : Coimbatore

School : School of Engineering

Department : Mechanical Engineering

Year : 2021

Abstract : The key objective of this research study is to examine the performance of TiAlSiN coated insert while performing dry machining of AISI 420 martensitic stainless steel on quantified output responses. This paper seeks to optimize process parameters namely speed, feed, and depth of cut during turning process, such as surface roughness, flank wear, and material removal rate simultaneously. TiAlSiN thin film was coated on the carbide tool through high power impulses magnetron sputtering. To confirm the existence of coated elements, SEM and XRD studies were performed. For coated and pure inserts, microhardness was measured, whereas the TiAlSiN coated tool possesses 43.34% higher than pure inserts. The dry machining was performed with three process parameters, each in three phases. The experimentation was performed based on Taguchi’s design of experiments (DoE). In this study, a Multi-Criteria decision making (MCDM) approach encompassing Data Envelopment Analysis based Ranking Methodology (DEAR) with Taguchi’s design was applied. The multi-response performance index (MRPI) was calculated and their impact on the machining parameters was scientifically examined. The parameter combination of cutting speed: 240 m/min; feed rate: 0.20 mm/rev and depth of cut: 0.50 mm was observed to be the optimal input parameters.

Cite this Research Publication : Moganapriya, C., Rajasekar, R., Mohanraj, T., Gobinath, V.K., Kumar, P.S. and Poongodi, C., 2021. Dry machining performance studies on TiAlSiN coated inserts in turning of AISI 420 martensitic stainless steel and multi-criteria decision making using Taguchi-DEAR approach. Silicon, pp.1-14.

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