Abstract
Artificial intelligence techniques have proven to be the better tool for modelling and optimisation of processes having complex nature and non-linear behaviour. It has been found that the hybrid approach of two individual tools may even perform more efficiently. The present paper proposes a computer aided hybrid approach of neural network and genetic algorithm for modelling and optimisation of different quality characteristics in a manufacturing process. The results of the developed software computer-aided-hybrid-neural-GA (CAHNG) has been compared with that of published literature and found suitable. Further, this software has been applied for modelling and optimisation of material removal rate and average temperature in electric discharge diamond grinding of high speed steel. The results show the considerable improvements in both the characteristics.
Published Version
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