Experimental study and predictive modelling of cold compaction green density in powder metallurgy of stainless steel components

Abstract

We experimentally obtain and analyse green density distribution in stainless steel compact samples and investigate the effect of compaction pressure on sample green density and density distribution. Experimental measurements of local density of stainless steel samples are conducted using scanning electron microscopy. For design purposes, the measured local densities, depth and planar location and compaction pressure are used to train an artificial neural network model to estimate the compaction density as a function of input parameters. Material parameters obtained experimentally are used to calibrate a finite element model. The results show that the artificial neural network and finite element modelling approaches are feasible and could be used in predicting the overall compaction density variations in powder metallurgy components. It is observed that the overall compact green density increases almost linearly with compaction pressure. Phenomena of particle interlocking and cold welding are observed and discussed.

Keywords:

• Powder metallurgy
• Cold compaction
• Green density
• Stainless steel components
• Finite element modelling
• Artificial neural network

This work was financially supported by a grant from NVE/Auto 21 of Canada. Authors are indebted to L. Peace of Powder-Tech Associates, Inc. for manufacturing of specimens.