Design and modelling of metal matrix composites

Objectif

Excellent mechanical properties make metal matrix composites (MMCs) an attractive and desirable material in many industries. However, manufacturing costs of MMCs are currently very high mainly due to lack of material design database and limited knowledge related to their behavior in various working conditions. This proposed research aims to improve understanding of the relation between MMCs production parameters and their properties. The proposed research plan has two main assignments: firstly, to experimentally obtain optimal processing parameters for producing highly conductive, strengthened copper matrix composites with uniform dispersion of submicron and nano-sized reinforcements; secondly, to create a computational model for mechanical alloying process and for predicting the behaviour of MMCs. Powder metallurgy will be used for MMCs production. It is expected that with increasing mechanical alloying time the distribution of reinforcements (in-situ formed during densification process) in metal matrix become more uniform which is a requirement for excellent mechanical properties. Investigating the influence of size and volume fraction of in-situ formed reinforcing nanoparticles on its microstructural, mechanical and physical properties will be the overall objective of the experimental work. Proposed techniques, data collection, and analysis will identify the relationship between process parameters and material behaviour, which will contribute to the establishment of process parameters for fabrication of MMCs. Obtained results will present a good database which can accelerate further research, development and possible implementation of MMCs. Moreover, creating computational models for control of microstructure and process design of MMCs will contribute to a better understanding and predicting the behaviour of a wide variety of MMCs. It will provide a cost effective solution in the manufacturing of MMCs which will expand possibilities in the design of new products.