Tool condition monitoring during hard turning of AISI 52100 Steel: A case study

Abstract

Quality of work material, the accuracy of finished parts, the roughness of the turned surface and wear at tool surface are greatly influenced by cutting tool vibrations. Therefore, tool condition monitoring study is the crucial aspect of machining. This paper deals with an online monitoring of flank wear and roughness of turned surface while turning of AISI 52100 hardened bearing steel by means of multilayer carbide insert (coated) under dry condition considering process parameters and vibration signals. Cutting speed is predominant factor for flank wear, surface roughness and acceleration amplitude of vibration. Cutting speed and vibration signals have strong correlations with the flank wear and surface roughness as Pearson correlation coefficients are positive. Prediction models through linear regression are significant taking into consideration the effect of both process parameters and vibration signals online in hard machining as percentage of error is relatively less.