Prediction of Mechanical Shear Force of Friction Stir Spot Welded Joints Using Neural Network System

Abstract:

An artificial neural network (ANN) system was created to analyze and simulate the relationship between process parameters of dissimilar weld joints of Aluminum alloy 6061 (AA60061) and pure copper and their resulting mechanical properties. In this study, 2.2 mm thick Aluminum Alloy 6061 and 1.4 mm thick pure copper lap joints are welded using friction stir spot welding (FSSW) process. Tensile-shear tests were performed to evaluate the mechanical characteristics of the lap joints. The welding process parameters are tool speed, plunge depth, and dwell weld time. Optimum friction stir spot welding (FSSW) parameters are identified to achieve the maximum shear load for Aluminum alloy (AA 6061) and pure copper lap joints. This is accomplished at a rotational speed of 2000 rpm for a duration of 20 seconds, with a plunge depth of 0.2 mm. At 15s dwell time and 2000 rpm tool speed, the shear load increases with increasing plunge depth. The best regression neural network that has the least mean squared error of 0.10192 and coefficient of correlation of 0.85033 is the model of 5 neurons in the hidden layer of the system

• Periodical: Key Engineering Materials (Volume 1012)
• Pages: 31-40
• DOI: https://doi.org/10.4028/p-tbUUi0
• Online since: May 2025
• Authors: Hamed A. Abdel-Aleem*, Ahmed M. Gaafer, A.M. Hanafy, El-Sayed H. Mansour
• Keywords: Aa 6061, Friction Stir Spot Welding, Neural Networks, Pure Copper

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