Physical and mechanical properties estimation of Ti HAP functionally graded material using artificial neural network

Abstract EN

This study presents the effort in applying neural network-based system identification techniques by using Back- propagation algorithm to predict some physical mechanical properties of functionally graded and composite samples from Ti/HAP, these samples were fabricated by powder metallurgy method at various volume fraction of hydroxyapatite and at n equal (0.8, 1, and 1.2).

Because of important of advanced materials such as FGMs as alternative industrial material, it is necessary to measure the physical properties of these materials such as porosity, density, hardness, compression …etc.

Therefore the ANN will be used to estimate these properties and give a good performance to the network