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Selecting the optimum process parameter level setting for multi-quality processes is cumbersome. Previous methods were plagued by complex computational search, unrealistic assumptions, ignoring the interrelationship between responses and failure to select optimum process parameter level settings. The methods of variable return to scale (VRS) back-propagation neural network (BPNN) previously adopted were limited by the use of weak models, poor discriminatory tendency and an inability to selectdoi:10.6084/m9.figshare.7296947.v1 fatcat:neq3qmrydzblpgsbiodmnn4duq