Experimental investigation into machinability of hardened AISI 4140 steel using TiN coated ceramic tool

• Effect of speed, feed and depth of cut on surface roughness and flank wear is studied.
• Machined surface morphology, tool wear mechanism and chip morphology are analyzed.
• Flank wear was due to abrasion and saw tooth chip was due to periodic crack.
• Mathematical models for Ra, Rq, Rz and VB are developed using RSM.
• Optimal range of surface roughness and flank wear are predicted with 95% CI level.

The concerns with some machinability aspects on surface roughness, flank wear and chip morphology in hard turning of AISI 4140 steel using PVD-TiN coated Al2O3 + TiCN mixed ceramic inserts under dry environment. The machined surface characterization, tool wear mechanism and chip morphology are investigated in this study, along with optimization and development of mathematical models for surface roughness and flank wear. By adopting combined techniques such as orthogonal array and analysis of variance, the consequences of cutting parameters (cutting speed, feed and depth of cut) on surface roughness (Ra, Rq and Rz) and flank wear (VB) are explored. The results show that feed is the principal cutting parameter influencing surface roughness, followed by cutting speed. However, flank wear is affected by the cutting speed and interaction of feed-depth of cut, although depth of cut has not been found statistical significant, but the flank wear is an increasing function of depth of cut. Thereafter, observations are made on the machined surface, worn tool and the generated chips by scanning electron microscope (SEM) to establish the process. Abrasion was the major wear mechanism found during hard turning within the studied range. Chip morphology indicates the formation saw-tooth/serrated chips at higher feed due to reduction of chip thickness, results in degradation of surface finish. Additionally, effect of tool wear on surface roughness has been studied. The experimental data were further analyzed to predict the optimal range of surface roughness and flank wear. Finally, based on response surface methodology (RSM) mathematical models are developed for surface roughness (Ra, Rq and Rz) and flank wear (VB) with 95% confidence level. Effectiveness, adequacy, statistical significance and validity, and fit of data of the developed model has been checked using ANOVA analysis (depending on P value, F value and R2 value), Anderson–Darling test and normal probability plot. The proposed experimental and statistical techniques initiate authentic methodologies to optimize, model and improve the hard turning process, which can be prolonged effectively to analyse other machining processes.