Identification of optimum laser beam welding process parameters for dissimilar steels butt joint based on AI Algorithms SA, GA and WOA

Authors

  • Bijivemula Narayana Reddy Sai Rajeswari Institute of Technology Proddatur, Andhra Pradesh, India
  • P. Hema S. V. University College College of Engineering, Tirupati, Andhra Pradesh, India
  • S. M. Jameel Basha Chadalawada Ramanamma Engineering College, Tirupati, Andhra Pradesh, India

DOI:

https://doi.org/10.58368/MTT.24.3-4.2025.12-22

Keywords:

Artificial Intelligence, Laser Beam Welding, Genetic Algorithm, Simulated Annealing Algorithm, Whale Optimization Algorithm

Abstract

In the 4th Industrial Revolution is implemented with the help of advances in Artificial Intelligence (AI). The effect of the AI has automatized the mechanical engineering with the smart machines and robots. The technology used these days in the machines is to aid the solutions in the direction of the real-world and complex problems. In the Industrial Revolution, manufacturing the better quality product with the high speed and accuracy is possible with advanced manufacturing by using laser beam. Laser is used to weld the similar/dissimilar with optimum strength and Heat Affected Zone. AI is used to find the better manufacturing process parameters of the Laser beam welding process. In this research paper, the laser beam welding is used to weld the AISI 316 and AISI 4130 steel metals and identified the optimal combination with the AI algorithms Genetic Algorithms, Simulated Annealing, and Whales Optimization Algorithm.

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References

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Reddy, B. N., Hema, P., & Padmanabhan, G. (2021). Experimental investigation on similar and dissimilar alloys of stainless steel joints by laser beam welding. Advances in Materials and Processing Technologies. 8. 1-16. 10.1080/ 2374068X.2020.1865125

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Published

01-03-2025

How to Cite

Reddy, B. N., Hema, P., & Jameel Basha, S. M. (2025). Identification of optimum laser beam welding process parameters for dissimilar steels butt joint based on AI Algorithms SA, GA and WOA. Manufacturing Technology Today, 24(3-4), 12–22. https://doi.org/10.58368/MTT.24.3-4.2025.12-22

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