Mechanical and Corrosion Studies of Friction Stir Welded Nano Al2O3 Reinforced Al-Mg Matrix Composites: RSM-ANN Modelling Approach

Chandrashekar A,B V Chaluvaraju,Sagr Alamri,Denis A Vinnik,Asif Afzal,Vineet Tirth,Ahamed Saleel C,Abdul Razak Kaladgi

doi:10.3390/sym13040537

Abstract

Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SEM analysis. The reinforcement particles present in the matrix were analyzed through energy-dispersive X-ray spectroscopy method. The properties (corrosion and mechanical) of the fabricated composites were evaluated. The mechanical and corrosion properties of the prepared nanocomposites initially increased and then decreased with the addition of nano Al2O3 particles in Al-Mg Matrix. The studies show that, the presence of 6 wt% of nano Al2O3 particles in the matrix improved the properties of other combinations of nano Al2O3 in the Al-Mg matrix material. Further, the Al-Mg/Al2O3 (6 wt%) MMNCs are joined by friction stir welding and evaluated for microstructural, mechanical, and corrosion properties. Al-Mg/Al2O3 MMNCs may find applications in the marine field. The response surface method (RSM) was used for the optimization of tensile strength, Young’s modulus, and microhardness of the synthesized material which resulted in a 95% of statistical confidence level. Artificial neural network (ANN) analysis was also carried out which perfectly predicted these two properties. The ANN model is optimized to obtain 99.9% accurate predictions by changing the number of neurons in the hidden layer.

Highlights

A large variety of methods are available commercially for the synthesis of nanoparticles and these are based on the expense of nanoparticles’ production, easiness, and morphological features
It is interesting to note that the Al2O3 nanoparticles are not visible in the X-ray diffraction peak in the case of Al-Mg/2 wt% Al2 O3 nanocomposite while in other nanocomposites it is visible at of 38
Al2O3(0–8 dispersed wt. %) of composites were fabricated by two-step stir

Summary

Introduction

A large variety of methods are available commercially for the synthesis of nanoparticles and these are based on the expense of nanoparticles’ production, easiness, and morphological features Among these methods, an auto-proliferating combustion process is considered to be the best alternative as by following this simple method, the required phase can be obtained with greater control over stoichiometric ratio [1,2,3]. An auto-proliferating combustion process is considered to be the best alternative as by following this simple method, the required phase can be obtained with greater control over stoichiometric ratio [1,2,3] This method of combustion synthesis requires the application of temperatures, where organic material is utilized to initiate the decomposition process of precursor metal salts [3,4,5]. Cluster formation and heterogeneous distribution of reinforcement throughout the medium due to casting or powder metallurgy manufacturing techniques is a common problem associated with aluminum matrix composites (AMCs)

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Conclusion