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

• Published in: Measurement : journal of the International Measurement Confederation Vol. 122; pp. 380 - 391
• Main Authors: Mia, Mozammel, Dey, Prithbey R., Hossain, Mohammad S., Arafat, Md T., Asaduzzaman, Md, Shoriat Ullah, Md, Tareq Zobaer, S.M.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.07.2018; Elsevier Science Ltd
• Subjects: Abrasion; Analyzers; Carbide tools; Cemented carbides; Cutting parameters; Cutting speed; Cutting wear; Design optimization; Feed rate; Hard turning; Imagery; Lubrication; Material removal rate; Material removal rate (machining); Measurement; Minimum quantity lubrication; Quantitative analysis; Roughness parameters; Scientific apparatus & instruments; Surface roughness; Taguchi S/N-based optimization; Tool wear; Turning (machining); Variance analysis; Wear mechanisms; Wear rate; Weights & measures; Minimum quantity lubrication; Material removal rate; Taguchi S/N-based optimization; Roughness parameters; Tool wear; Hard turning
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2018.02.016

[Display omitted] •Optimization of surface roughness, tool wear and MRR.•Investigations of tool wear mechanism in turning 40 HRC steel under MQL.•vc = 90 m/min, f = 0.2 mm/rev, ap = 1.5 mm optimized roughness and MRR.•vc = 60 m/min, f = 0.2 mm/rev, ap = 1.0 mm minimized tool flank wear.•Prominent tool wear mechanisms are adhesion, abrasion and BUE. Minimum quantity lubrication (MQL) is adorned with improved machining performance and environmental sustainability. In this context, this paper presents the study of roughness parameters (Ra, Rq, Rz), tool wear parameters (VB, VS) 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.