Multi-Response Optimization of Robotic Welding Parameters Using a Taguchi-Based Random Forest Model for Dissimilar Joining Materials

Authors

  • Amin Lawong Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Thailand
  • Surachai Nampromma Department of Industrial Technical Education, Faculty of Technical Education, Rajamangala University of Technology Isan, Khon Kaen Campus, Thailand
  • Thaithat Sudsuansee Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Thailand
  • Supakit Sergsiri Department of Industrial Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Thailand
Volume: 15 | Issue: 5 | Pages: 27237-27243 | October 2025 | https://doi.org/10.48084/etasr.12124

Received: 13 May 2025 | Revised: 16 June 2025 | Accepted: 28 June 2025 | Online: 6 October 2025


Corresponding author: Supakit Sergsiri

Abstract

Multi-Response Optimization (MRO) is critical for resolving conflicting criteria in robotic welding processes, particularly in dissimilar metal joining, where optimizing weld hardness typically conflicts with minimizing heat input. This study aims to optimize Gas Metal Arc Welding (GMAW) parameters for joining malleable ductile cast iron with AISI 1045 steel. A novel hybrid methodology is proposed, integrating the Taguchi experimental design (L9 orthogonal array), a multi-output Random Forest regression model, and a weighted scoring function. Results quantitatively identified optimal parameters (current = 135 A, voltage = 27.5 V, travel speed = 36 cm/min), achieving hardness of 203.85 HV and heat input of 6.27 kJ/cm, with a normalized score of 0.9061. Qualitative expert evaluation validated the approach's practicality and accuracy. The findings highlight the benefits of integrating classical experimentation with Artificial Intelligence (AI)-driven modeling to facilitate precise, data-driven decision-making in manufacturing optimization.

Keywords:

robotic welding, multi-response optimization, Random Forest, Taguchi method, dissimilar metal joining

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How to Cite

[1]
A. Lawong, S. Nampromma, T. Sudsuansee, and S. Sergsiri, “Multi-Response Optimization of Robotic Welding Parameters Using a Taguchi-Based Random Forest Model for Dissimilar Joining Materials”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27237–27243, Oct. 2025.

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