Predictive Modelling of Tool Wear in Hard Turning of High-Alloyed Steel Using Design of Experiments and Response Surface Methodology
Authors: L. ZOUAMBI, M. BOURDIM, S. KERROUZ
DOI: 10.37326/ajsev8.10/2054
Page No: 52-68
Abstract
Tool wear prediction remains a critical challenge in precision machining operations, directly affecting surface quality, dimensional accuracy, and manufacturing costs. This study presents a comprehensive mathematical model for predicting flank wear (VB) in cubic boron nitride (CBN) tool inserts during longitudinal turning of X155CrMoV12 high-alloyed steel. A full factorial design of experiments (DOE) approach was employed to investigate the individual and interactive effects of three cutting parameters: cutting speed (90-220 m/min), feed rate (0.08-0.25 mm/rev), and depth of cut (1-2.6 mm). Eight experimental trials were conducted on a TOS SN40C universal lathe, with wear measurements performed using precision microscopy (±1μm accuracy). A first-order polynomial regression model was developed using Design Expert 7 software and validated through MATLAB programming. Statistical analysis revealed that cutting speed is the predominant factor influencing tool wear, followed by feed rate and depth of cut. The predictive model demonstrated excellent correlation with experimental data, with iso-response curves clearly illustrating the wear progression across the parameter space. Results indicate that VB wear increases proportionally with cutting speed, ranging from 0.071 mm at optimal low-speed conditions to 0.290 mm under aggressive cutting parameters. The validated mathematical model enables accurate wear prediction within the experimental domain, facilitating optimal cutting parameter selection to extend tool life by up to 75% while maintaining surface integrity. This research provides manufacturing engineers with a practical decision-support tool for optimizing hard turning operations of high-alloyed steels, contributing to sustainable machining practices through reduced tool consumption and improved process efficiency.
