Physical and Mechanical Properties Estimation of Ti/HAP Functionally Graded Material Using Artificial Neural Network

Abstract

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. INTRODUCTION n artificial neural network (ANN), often just called a neural network, is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation [1]. Complex nonlinear input-output relationships that used in many applications can be applied on neural network because the important feature of neural networks is that they have the ability to learn complex nonlinear input-output relationships, many steps can be applied as the sequential training procedures, and adapt themselves to the data. Many applications of neural networks, pattern classification tasks are represented in [2,3]. Classification and clustering tasks can be done perfectly by learning process of the neural network which consist of updating network architecture and connection weights. Recently, pattern solving recognition problems have been depending on neural network models because of their apparently low dependence on domain-specific knowledge and due to using learning algorithms efficiently by practitioners. Neural network architectures hardware implementation can be mapped to hardware implementation by using electronic devices. The novel structure of the information processing system which is inspired by the way biological nervous systems, such as the brain, process information. It consists of a large number of neurons (processing components) which woks to solve definite problems. Learning in biological systems contains regulations process of the synaptic contacts that exist between the neurons, this learning can be used to adjust the weights in mathematical model, contact the artificial neurons each together for data classification and pattern recognition and recognition applications [4-8]. In the advanced materials applications, the functionally graded materials (FGMs) are used due to their significant characteristics like bio implant application. Due to the lack of suitable fabrication method for the FGMs, the impact of the outcomes was still limited until present time A