Prediction of Weld Geometry in Laser Welding by Numerical Simulation & Artificial Neural Networking

ABSTRACT

Advanced manufacturing processes have increased industrial productivity and enhanced marketing prospects. Optimization techniques coupled with simulative methods in laser welding can immensely contribute to the development of the industries of the future. This work reports the research results emanating from the application of simulative techniques for the prediction of welding parameters of the laser welding process of aluminium alloy 2024. Numerical simulation was conducted in a proprietary software environment and subsequently, artificial neural network (ANN)-based simulation was conducted. The simulatively obtained data were compared with experimentally obtained data and were found to be in good accord. The ANN was trained, validated, and tested with the experimental data. The validity of the ANN-based model was ascertained with linear regression analysis and the correlation coefficients were computed. A comparison was made between the numerical simulation-based model and the ANN-based model and it was observed that the artificial neural network-based model predicts the responses faster hence it could be viewed as a potent tool for quicker and more efficient calculation of the weld dimensions and strength in both laboratory and workshop environments.

KEYWORDS:

• Laser welding
• numerical simulation
• artificial neural networking
• lap joint
• weld geometry
• aluminium alloy

Disclosure statement

Table 2, figures 2a and 3 of this work have been reproduced from relevant portions of the publication entitled “Numerical Analysis of Conduction Mode Laser Welding of Aluminium 2024 Alloy in Lap Joint Configuration” (https://doi.org/10.1007/978-981-19-4208-2_12) with written permission accorded by the collegium of Upama Dey, Aparna Duggirala, Bappa Acherjee and Souren Mitra, the authors and copyright holders of that publication which was published by Springer Nature Singapore Pte. Ltd. in Advances in Manufacturing Engineering, Lecture Notes in Mechanical Engineering (2023) under an exclusive license. In addition, the necessary license (License Number: 5544641317849) has been obtained from Springer Nature for the re-use of pertinent material in this work. The first author would like to acknowledge Mr. Dipten Misra, Director, School of Laser Science and Engineering, Jadavpur University for kindly granting authorization to use laboratory facilities for the purposes of experimentation.