Optimisation of surface roughness in hard turning AISI D2 steel using TSK-type fuzzy logic rules

In the present work, an intelligent method is adopted to optimise the machining parameters to obtain a desired surface roughness on AISI D2 steel in Hard turning operations. In order to perform the turning operation a ceramic insert tool is used. The task of this optimisation is carried out by two stages: in the first stage, a rule-based model is constructed based on experimental (training) data, and later, a genetic algorithm (GA) is used to optimise the critical machining parameters based on this model as predictor. Developing a suitable model for a machining process is a difficult and primary task for optimisation of machining process. Due to non-linearity of the cutting parameters, tool-work combination and rigidity of machine tool, it has been shown that mathematical or analytical approaches failed to develop models for manufacturing processes.

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