Modeling of dimensional changes during sintering

Abstract

An approach to modeling the behavior of dimensions of PM parts during the sintering process for the prediction of dimensional changes is given. The model is developed on the basis of significant process factors by applying a multilayer neural network architecture with the backpropagation learning algorithm. Results of the simulation in the form of diagrams and tables are presented. The presented model gives better results than the one based on statistical analysis of experimental data, i.e. less total mean approximation errors of the part dimensions for 11.4%. A practical result of the model is the determination of compact dimensions to compensate for dimensional changes during sintering. .