Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition

Minimum quantity lubrication (MQL) is adorned with improved machining performance and environmental sustainability. In this context, this paper presents the study of roughness parameters (R-a, R-q, R-z), tool wear parameters (V-B, V-S) and material removal rate (MRR) in MQL-assisted hard turning by using coated cemented carbide tool. As methodology, the Taguchi orthogonal array-based design of experiment and signal-to-noise ratio-based optimization have been utilized. Prior to this, the collected machining data have been tested to check the normal probability distribution. Furthermore, the analysis of variance determined the influences of cutting speed, feed rate and depth of cut on the aforementioned responses. In addition, different types of tool wear, prevailed in principal and auxiliary flank faces, have been identified. The quantitative analysis revealed that the cutting speed impacted the surface roughness; the depth of cut influenced the tool wear; the feed rate afflicted the material removal rate predominantly. Based on the imagery evidence the adhesion, abrasion, and built-up-edge were found as the governing wear mechanism.