Knowledge based and adaptive computational techniques for concurrent design of powder metallurgy parts

Abstract

The practice of concurrent engineering (CE) is adopted widely to facilitate integrated design and manufacture by industry in order to maintain competitiveness in the market place. Use of near net-shape processes like powder metallurgy (PM) within a CE environment can give added benefits in terms of material utilisation and environmental considerations. A system for concurrent design of PM parts is described here. It is developed to assist material and process selection while optimising the design for a part to be manufactured using the cold-compaction process. The system uses a rule based design geometry evaluations program to assess the parts' suitability for cold compaction. Design data in B-rep format conforming to the STEP standard is employed in this work. Material and process parameter selections are performed using a hybrid system employing Bayesian neural networks and heuristics. By employing an integrated system to aid material selection and manufacturability analysis during the early design phase, part designers can reduce the number of design iterations, improve quality of designs, and minimise trials required in PM part manufacture.