Abstract

Due to the existence of diverse selective inhibition sintering (SIS) processing variables and intricate stochastic nature, arriving optimal processing conditions to enhance the product quality is extremely difficult. This paper concentrates on the development of SIS system model to predict the optimal SIS process variables to improve the dimensional accuracy. Response surface methodology (RSM) is employed to design the experiments and develop the mathematical models by considering various SIS process parameters. The developed regression models are further optimized by an evolutionary approach of genetic algorithm (GA) scheme. The proposed approach can be effectively utilized to predict the dimensional accuracy under various process conditions.

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