Abstract
Rapid tool wear is one of the most important problems in rough turning of Inconel 625 nickel-based super alloys which is less considered as a case study in wear analysis researches. Therefore, in this study, high material removal rate and low tool wear of PVD-TiAlN coated carbide were considered as objective factors in rough turning of Inconel 625 super-alloy. By using an adaptive network-based fuzzy inference system; an input-output relationship model was developed to find the effective parameters. Furthermore, simulated annealing algorithm method was used to define an optimum cutting condition considering low flank wear and high material removal rate. The results indicate that cutting speed and depth of cut with their interaction are the most effective factors on tool wear. While, feed rate is almost ineffective on tool wear propagation which can be increased to obtain higher material removal rate.
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