Modelling of tool life and surface roughness in hard turning using soft computing techniques: a comparative study

In this paper the potential of soft computing techniques for tool wear and surface roughness prediction in hard turning operations under high pressure cooling conditions using coated carbide tools was investigated. An experimental investigation was conducted to analyse the effects of various cutting conditions on these two parameters analysed in the hard turning of the 100Cr6 steel (62 HRC). On the basis of experimental results two different methods, namely, artificial neural network (ANN) and adaptive neuro–fuzzy inference system (ANFIS) are developed for tool wear and surface roughness prediction. The estimation results obtained by both models are compared with experimental results and very good agreement is observed.