Applying multi-response optimization for sustainable machining of 316 stainless steel with coconut oil assisted minimum quantity lubrication

Kadia, Rajendrakumar V, Dundurb, Suresh T, Goudar, Dayanand M and Haider, Julfikar ORCID: https://orcid.org/0000-0001-7010-8285 (2023) Applying multi-response optimization for sustainable machining of 316 stainless steel with coconut oil assisted minimum quantity lubrication. Tribology: Materials, Surfaces and Interfaces, 17 (1). pp. 48-61. ISSN 1751-5831

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Abstract

Environmental machining was investigated using coconut oil and minimal quantity lubrication (MQL) in the turning of AISI 316 stainless steel. The turning parameters and MQL flow rate were optimized using ANOM and ANOVA in multi-response analysis to produce the best hardness and minimum surface roughness in the machined surface of AISI 316 stainless steel. The feed, speed, depth of cut, and MQL flow rate during turning were used as the input parameters and surface roughness and hardness as the output parameters. The experimental plan was developed using Taguchi's L9 orthogonal array. It was found that minimum surface roughness (Ra: 1.12 µm and Rz: 6.37 µm) was achieved at a cutting speed of 120 m/min, feed rate between 0.25 to 0.3 mm/rev, the depth of cut between 1.0 to 1.5 mm and a MQL flow rate of 90 ml/hr. Micro hardness was measured from the machined surface to a depth of 1.075 mm to determine the machining affected zone (MAZ). It has been noted that the hardness reduced with an increase in machined surface depth. The MQL with coconut oil was shown to be an ecofriendly lubrication method for machining difficult-to-cut materials like stainless steel and keeping good surface integrity.