Title of Thesis

Modeling Of High Strength And Wear Resistance Aluminum Alloy Based Casting Composite Material

Author(s)

Jokhio Muhammad Hayat

Abstract
Modeling of high strength and wear resistance aluminum alloy based casting of composite material developed via conventional foundry method which is one of the most economical versatile and active research area and so for has not been thoroughly investigated.
Due to complex nature of the composite materials and their related problems such as the nonlinear relationship between composition, processing parameters, heat treatment with the strength and abrasive wear, resistance can more efficiently be modeled by artificial neural networks. The artificial neural networks modeling requires sufficient data concerned with chemical composition , processing parameters and the resulting mechanical properties which were not available for such type of modeling.
Therefore, a wide range of experimental work was conducted for the development of aluminum composites using conventional foundry method. Alloy containing Cu-Mg-Zn as matrix and reinforced with 1- 15 % Al2O3 particles were prepared using stir casting method. The molten alloys composites were cast in metal mold. More than eighty standard samples were prepared for tensile tests and sixty samples were given solution treatment at 580 0C for ½ hour and tempered at 120 0C for 24 hours.
Various characterization techniques apparatus such as X-ray Spectrometer, Scanning Electron Microscope, Optical Metallurgical Microscope, Universal Tensile Testing Machine, Vickers Hardness and Abrasive Wear Testing Machine were used to investigate the chemical composition, microstructural features, density, tensile strength, ductility (elongation), hardness and abrasive wear resistance.
These investigations including the material development and characterization were used for data generations as needed for modeling of high strength and abrasive wear résistance aluminum cast composites.
For modeling purpose a multilayer perceptron (MLP) feedforward was developed and back propagation learning algorithm was used for training, testing and validation of the model.
The modeling results shows that an architecture of 14 inputs with 9 hidden neurons and 4 outputs which include the tensile strength, elongation, hardness and abrasive wear resistance gives reasonably accurate results with an error within the range of 2-7 % in training, testing and validation.The modeling results shows that an alloy contents 2-3 % Cu, 2-3 % Mg, 3-5 % Zn reinforced with 10 % Al2O3 can successfully be developed for highest strength (297 MPa) and highest abrasive wear résistance (0.4 gm weight loss /15 minutes using stir casting method. The modeling results also suggest that it is possible to develop the highest strength 466 MPa tensile strength and highest abrasive wear resistance aluminum alloy based casting composite materials having the matrix composition of 6 % Si, 2 % Mg with 3 % Zn reinforced with 2-5 % Al2O3 particles.

1

1.1 Theoretical Background
1.2 Scope of the Study
1.3 Objectives of the Study
1.4 Structure of the Thesis

2.1 Introduction
2.2 Aluminium Métal Matrix Composites (AMMCS)
2.3 Properties of Stir Casting of Aluminum Composites
2.4 Factors Affecting the Strength and Abrasive Wear
2.5 Modeling
2.6 Neural Network in Materials Science and Engineering

3.1 Introduction
3.2 Design and Fabrication of Experimental Rig
3.3 Alloys Development Strategy
3.4 Samples Preparation
3.5 Precipitation Treatment Processes
3.6 Tensile Tests
3.7 Chemical Analysis
3.8 Hardness Tests
3.9 Abrasive Wear Tests
3.10 Measurement of Density and Porosity
3.11 Metallography
3.12 Modeling Methodology

4

4.1 Introduction
4.2 Chemical Composition
4.3 Tensile Strength and Ductility (Elongation)
4.4 Hardness
4.5 Abrasive wear Resistance
4.6 Density and Porosity
4.7 Microstructure
4.8 Modeling and Training

5

5.1 Material Development
5.2 Modeling
5.3 Suggestions for Future Work

6