Application of Artificial Neural Network and Response Surface Methodology for Mechanical Property Prediction and Optimization in Aluminum Hull Welding for Alloy 5083 Grade

Journal article

Authors/Editors

Strategic Research Themes

Publication Details

Author list: Prachya Peasura;Suthipong Sopha

Publisher: คณะวิศวกรรมศาสตร์ มหาวิทยาลัยเชียงใหม่

Publication year: 2020

Volume number: 27

Issue number: 2

Start page: 201

End page: 215

Number of pages: 15

ISSN: 0857-2178

eISSN: 2672-9695

URL: https://researchs.eng.cmu.ac.th/UserFiles/File/Journal/27_2/15.pdf

Abstract

This research was aimed to determine a mathematic model using artificial neural network (ANN) for predicting the of mechanical property and optimization using response surface methodology (RSM) in the aluminum hull 5083 grade with gas metal arc welding (GMAW) process. The following welding factors were studied: the welding current, voltage and travel speed. The resulting welding samples were examined using tensile strength tests hardness test which were observed microstructure with scanning electron microscopy (SEM) and determine a suitable mathematic model. The research results reveal that using a ANN model with the proposed mathematical model, which tensile strength and hardness represents 3 neurons for the input layer 10 neurons for hidden layer 1 10 neurons for hidden layer 2 and 1 output neurons (3-10-10-1). The Levenberg-Marquart training algorithm was also train for weight and bias network. The neuron of log-sigmoid for input layer, tan-sigmoid for hidden layer1 and 2 purelin for output layer activation function was assigned. The mean square error (MSE) and coefficient of determination (R2) for tensile strength and hardness predict was showed that of 0.454 and 0.386 respectively. The optimization of GMAW parameters were welding current of 220 amp, voltage of 26 V and 10 mm

Keywords

การเชื่อมลำเรืออลูมิเนียม, โครงข่ายประสาทเทียม, วิธีพื้นผิวตอบสนอง, สมบัติทางกล