Abstract— In turning process, the machinability andtool wear are mainly stochastic rather than deterministic because of itscomplexity in nature. Tool change strategies are now based on the mostconservative estimate of tool life from the past tool wear data. Always acomplex relationship exists between various process parameters viz., speed,feed, depth of cut, cutting time, tool geometry and cutting forces. Hence thereis a need to develop models, which can capture this complex interrelationshipbetween the parameters. In the present work, an empirical relationship has beendeveloped between these parameters based on the experimental data.
By consideringspeed, feed, depth of cut and cutting time as the input variables, the cuttingforces and the flank wear were found out experimentally for a given tool -material combination. Based on these experimental data, a mathematical modelhas been developed. This model can be applied to all circumstances to estimatethe cutting forces and the flank wear.
An artificialneural network model has also been developed to estimate the flank wear undervarying cutting conditions. The results achieved by both the approaches havebeen compared and found to be closely related.
Keywords:- turning, flank wear, cutting force, neural network.