Surface integrity predictions and optimisation of machining conditions in the turning of AISI H13 tool steel

Abstract

Surface integrity (SI) plays a very important role in functional performance. It is dependent on a large number of machining parameters. The major concern of industry is to know which combination of machining parameters provides the best SI of machined components. Traditionally, surface roughness is considered to be the principal parameter to assess the SI of a machined part. However, residual stresses also become an important parameter because they control the lifetime of components (moulds, dies, etc.) and their abilities to withstand severe thermal and mechanical cyclic loadings (fatigue) during service. Therefore, significant improvements in the quality of the mould/die can be achieved with the control of residual stresses and surface roughness, both induced by machining. This paper examines both residual stresses and surface roughness induced by the dry turning of AISI H13 tool steel with different hardnesses. SI parameters were evaluated experimentally with respect to tool geometry, cutting speed, feed and depth of cut. A modelling and optimisation procedure based on artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA) approaches was developed and applied to identify the optimum combination of cutting parameters, leading to the best SI for machined components.