A neural network approach for selection of powder metallurgy materials and process parameters

Abstract The artificial neural network (NN) methodology presented in this paper has been developed for selection of powder and process parameters for Powder Metallurgy (PM) part manufacture. This methodology differs from the statistical modelling of mechanical properties in that it is not necessary to make assumptions regarding the form of the functions relating input and output variables. Employment of a NN approach allows specification of multiple input criterion, and generation of multiple output recommendations. The inputs comprise the required mechanical properties for the PM material. The system employs this data within the NN in order to recommend suitable metal powder compositions and process settings. Comparison of predicted and experimental PM materials data has confirmed the accuracy of the NN approach, for predicting the materials and process settings needed for attainment of required process outcomes.