A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization

Taher A Shehabeldeen,Ammar H Elsheikh,Xiaoyuan Ji,Xu Shen,Mohamed Abd Elaziz,Osama Farouk Hassan,Jianxin Zhou,Yajun Yin

doi:10.1109/access.2020.2990137

Abstract

Aluminum alloys have low weldability by conventional fusion welding processes. Friction stir welding (FSW) is a promising alternative to traditional fusion welding techniques for producing high quality aluminum joints. The quality of the welded joints is highly dependent on the process parameters used during welding. In this research, a new approach was developed to predict the process parameters and mechanical properties of AA6061-T6 aluminium alloy joints in terms of ultimate tensile strength (UTS). A new hybrid artificial neural network (ANN) approach has been proposed in which Henry Gas Solubility Optimization (HGSO) algorithm has been incorporated to improve the performance of Random Vector Functional Link (RVFL) network. The HGSO-RVFL model was constructed with four parameters; rotational speed, welding speed, tilt angle, and pin profile. The validity of the model was tested, and it was demonstrated that the HGSO-RVFL model is a powerful technique for predicting the UTS of friction stir welded (FSWD) joints. In addition, the effects of process parameters on UTS of welded joints were discussed, where a significant agreement was observed between experimental results and predicted results which indicates the high performance of the model developed to predict the appropriate welding parameters that achieve optimal UTS.

Highlights

COMPARISON OF EXPERIMENTAL RESULTS WITH PREDICTED RESULTS OF Henry Gas Solubility Optimization (HGSO)-Random Vector Functional Link (RVFL), Adaptive Neuro-Fuzzy Inference System (ANFIS), AND K Nearest Neighbor algorithm (KNN) MODELS FOR ultimate tensile strength (UTS) In this study, we proposed HGSO-RVFL as the main algorithm
The correlation between HGSO-RVFL predicted data and the corresponding experimental data is better than those obtained by ANFIS or KNN thanks to the integration between advanced artificial neural network (ANN) technique (RVFL) and robust metaheuristic technique (HGSO) that successfully predicts the UTS of Friction stir welding (FSW) process
Since the HGSO aims to find the optimal configuration that provides RVFL with a suitable tool to avoid the limitations in traditional ANFIS and KNN such as absence of direct link between input and output which leads to overfitting

Summary

Introduction

It has a relatively high strength with good toughness and high corrosion resistance. The associate editor coordinating the review of this manuscript and approving it for publication was Fan Zhang. In aerospace, automotive, and marine industries [1], [2]. Intensive structural applications of AA6061 such as truck roofs, side panels, and ship bodies require feasible and efficient welding techniques. Conventional fusion welding of aluminum alloys is unsuitable due to creation of intermetallic compounds, oxide layers, hot cracks in molten weld pool which deteriorate the joint mechanical properties such as strength, hardness, toughness, stiffness, and

Methods

Results

Conclusion