The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

Highly automated CNC end milling machines in manufacturing industry requires reliable model for prediction of tool flank wear. This model later can be used to predict the tool flank wear (VBmax) according to the process parameters. In this investigation an attempt was made to develop an empirical relationship to predict the tool flank wear (VBmax) of carbide tools while machining LM25 Al/SiCp incorporating the process parameters such as spindle speed (N), feed rate (f), depth of cut (d) and various % wt. of silicon carbide (S). Response surface methodology (RSM) was applied to optimizing the end milling process parameters to attain the minimum tool flank wear. Predicted values obtained from the developed model and experimental results are compared, and error & LT; 5 percent is observed. In addition, it is concluded that the flank wear increases with the increase of SiCp percentage weight in the MMC.