Enhancing Friction Stir Welding in Fishing Boat Construction through Deep Learning-Based Optimization

Erfan Maleki

Mechanical Engineering Department, Politecnico di Milano, Milan, 20156, Italy

Okan Unal

Mechanical Engineering Department, Karabuk University, Karabuk, 78050, Turkey; Modern Surface Engineering Laboratory, Karabuk University, Karabuk, 78050, Turkey

Seyed Mahmoud Seyedi Sahebari

Department of Mechanical and Manufacturing Engineering, Ontario Tech University, L1G 0C5ON, Oshawa, Canada

Kazem Reza Kashyzadeh

Department of Transport, Academy of Engineering, RUDN University, 6 Miklukho-Maklaya Street, Moscow, 117198, Russian Federation

Nima Amiri

Paul M. Rady Department of Mechanical Engineering, University of Colorado, Boulder, CO, 80309, United States

DOI: https://doi.org/10.36956/sms.v5i2.875

Received: 17 June 2023; Revised: 10 August 2023; Accepted: 18 August 2023; Published: 19 August 2023

Copyright © 2023 Erfan Maleki, Okan Unal, Seyed Mahmoud Seyedi Sahebari, Kazem Reza Kashyzadeh, Nima Amiri . Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


In the present study, the authors have attempted to present a novel approach for the prediction, analysis, and optimization of the Friction Stir Welding (FSW) process based on the Deep Neural Network (DNN) model. To obtain the DNN structure with high accuracy, the most focus has been on the number of hidden layers and the activation functions. The DNN was developed by a small database containing results of tensile and hardness tests of welded 7075-T6 aluminum alloy. This material and the production method were selected based on the application in the construction of fishing boat flooring, because on the one hand, it faces the corrosion caused by proximity to sea water and on the other hand, due to direct contact with human food, i.e., fish etc., antibacterial issues should be considered. All the major parameters of the FSW process, including axial force, rotational speed, and traverse speed as well as tool diameter and tool hardness, were considered to investigate their correspondence effects on the tensile strength and hardness of welded zone. The most important achievement of this research showed that by using SAE for pre-training of neural networks, higher accuracy can be obtained in predicting responses. Finally, the optimal values for various welding parameters were reported as rotational speed: 1600 rpm, traverse speed: 65 mm/min, axial force: 8 KN, shoulder and pin diameters: 15.5 and 5.75 mm, and tool hardness: 50 HRC.

Keywords: Simulation and modelling, Welding, Friction stir welding, Deep neural network, Optimization


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