Title: Experimental investigation on cutting force and surface roughness in machining of hardened AISI 52100 steel using cBN tool

Authors: Sudhansu Ranjan Das; Amaresh Kumar; Debabrata Dhupal

Addresses: Department of Manufacturing Engineering, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India ' Department of Manufacturing Engineering, National Institute of Technology, Jamshedpur, 831014, Jharkhand, India ' Department of Production Engineering, Veer Surendra Sai University of Technology, Burla, 768018, Odisha, India

Abstract: The paper focused on finish dry hard turning of AISI 52100 steel with cBN tool by employing combined techniques (L₉ OA and ANOVA) to determine the effect of cutting parameters (cutting speed, feed and depth of cut) on cutting force (Fc) and surface roughness (Ra, Rz). The results show that feed and cutting speed strongly influence surface roughness; whereas depth of cut is the principal significant factor affecting cutting force followed by feed. The prediction of optimal range (at 90% CI) for Fc, Ra and Rz, along with multi-response optimisation (based on desirability function approach) was performed in order to optimise the cutting parameters. Thereafter, the mathematical models for each response are developed using multiple linear regression analysis and several diagnostic tests have been performed to check the validity, effectiveness, adequacy of the developed model. Simultaneously, the tool flank wear pattern, machined surface of the workpiece and generated chips were microscopically examined under optimum cutting condition.

Keywords: hard turning; AISI 52100 steel; CBN tools; cubic boron nitride; cutting force; surface roughness; analysis of variance; ANOVA; regression analysis; surface quality; dry turning; dry machining; cutting speed; feed rate; depth of cut; mathematical modelling; tool wear; flank wear.

DOI: 10.1504/IJMMM.2016.078997

International Journal of Machining and Machinability of Materials, 2016 Vol.18 No.5/6, pp.501 - 521

Received: 21 Apr 2015
Accepted: 05 Jan 2016
Published online: 08 Sep 2016 *

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