Sustainable Energy and Artificial Intelligence

Prediction of Structural Steel Strength with Lap Joint Welds Using Artificial Neural Networks and Finite Element Simulation

Document Type : Original Article

Author

Samaneh Pourolajal

Graduated from Bu-Ali Sina University, Hamedan, iran

https://doi.org/10.61186/seai.2506-1026
Abstract
Abstract
Welding is a critical and sensitive technique for joining parts in industry. It has numerous applications and, in some cases, is irreplaceable. Therefore, it's essential to examine components produced by welding to determine the tolerable strength of welded sections for design and manufacturing purposes.
This study utilized thermo-elasto-plastic finite element analysis to examine the thermomechanical behavior that emerges from the arc welding of overlapping 37St steel sheets. The welding process was simulated in two stages using ANSYS software. The weld strength was measured through two independent analyses: thermal and mechanical. To simulate the heat source during welding, the element "birth and death" feature was utilized. Due to the high temperature gradient in the weld zone, the material's thermophysical and mechanical properties were considered temperature-dependent.
The calculation of weld strength is of great importance. Given that experimental methods are time-consuming and costly, the provision of fast and economical methods is significant. In this article, weld strength has been calculated using two methods: simulation with ANSYS software and artificial neural networks with MATLAB software. Finally, the results obtained from these two methods (numerical simulation and artificial neural networks) have been compared with experimental results.

Keywords

Keywords: Welding
Lap Joint
Strength
Artificial Neural Network
Weld Simulation
Subjects
1. He, B. (2005). Computer modeling of weld joint microstructure and residual stresses (Doctoral dissertation, Carleton University).
2. Barsoum, Z., & Lundbäck, A. (2009). Simplified FE welding simulation of fillet welds–3D effects on the formation residual stresses. Engineering Failure Analysis, 16(7), 2281-2289.
3. Deng, D., Murakawa, H., & Liang, W. (2008). Numerical and experimental investigations on welding residual stress in multi-pass butt-welded austenitic stainless steel pipe. Computational Materials Science, 42(2), 234-244.
4. Malik, A. M., Qureshi, E. M., Dar, N. U., & Khan, I. (2008). Analysis of circumferentially arc welded thin-walled cylinders to investigate the residual stress fields. Thin-Walled Structures, 46(12), 1391-1401.
5. Ramírez, H., & Rubio-Gonzalez, C. (2006). Finite-element simulation of wave propagation and dispersion in Hopkinson bar test. Materials & Design, 27(1), 36-44.
6. Lahmy, S and Majzoobi, GH. (2013), Material model constants determined using Taylor's test, Hopkinson rod and neural networks. Master of Science Thesis, Bu-Ali Sina University, Faculty of Engineering, Department of Mechanical Engineering, 130-150.
7. Haykin, S. (2009). Neural networks and learning machines, 3/E. Pearson Education India.
8. Zhang, Z. (2018). Artificial neural network, Wiley Interdisciplinary Reviews: Computational Statistics, 10(2), e1438.
9. Toğrul, İ. T., & Pehlivan, D. (2004). Modelling of thin layer drying kinetics of some fruits under open-air sun drying process. Journal of Food Engineering, 65(3), 413-425.
10. Dayhoff, J. E., 1990. Neural Network Principles. Prentice-Hall International, U.S.A.
11. Khanna, T. (1990). Foundations of neural networks. Addison-Wesley Longman Publishing Co., Inc..
Sustainable Energy and Artificial Intelligence
Volume 1, Issue 3 - Serial Number 6
Summer 2025
Pages 157-164

  • Receive Date 01 June 2025
  • Revise Date 09 June 2025
  • Accept Date 10 June 2025
  • First Publish Date 10 June 2025

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us