Hybrid RSM–ANN–GA Optimization of TIG Hot-Wire Welding for AISI 5160 Steel

Phattharapong Keidlaphi; Sittichai Charonerat; Suriya Prasomthong; Teephet Chaiyason

doi:10.48084/etasr.17832

Authors

Phattharapong Keidlaphi Department of Manufacturing Engineering, Faculty of Engineering and Architecture, Rajamangala University of Technology Suvarnabhumi, Phra Nakhon Si Ayutthaya, Thailand
Sittichai Charonerat Department of Industrial Technology, Nakhon Phanom University, Nakhon Phanom, Thailand
Suriya Prasomthong Department of Industrial Technology, Nakhon Phanom University, Nakhon Phanom, Thailand
Teephet Chaiyason Department of Industrial Technology, Nakhon Phanom University, Nakhon Phanom, Thailand

Volume: 16 | Issue: 3 | Pages: 34879-34886 | June 2026 | https://doi.org/10.48084/etasr.17832

Received: 29 January 2026 | Revised: 8 March 2026 | Accepted: 15 March 2026 | Online: 5 April 2026

Corresponding author: Teephet Chaiyason

Abstract

Repair welding of AISI 5160 chromium spring steel is challenging due to its high sensitivity to thermal cycling, which may cause HAZ softening, carbide instability, and reduced toughness. This study proposes an integrated Response Surface Methodology–Artificial Neural Network–Genetic Algorithm (RSM–ANN–GA) framework for predicting and optimizing the multi-objective performance of TIG hot-wire repair welding, aiming to simultaneously enhance weld hardness and impact energy under limited experimental data. A compact Central Composite Design (CCD) with nine runs was employed, by varying Welding Current (WC) and Hot-Wire Current (HWC), while travel speed was kept constant. The measured responses ranged from 49.6–62.2 HRC and 19.8–35.5 J, indicating a strength–toughness interaction governed by heat-balance control. Quadratic RSM–CCD models assessed by ANOVA showed excellent adequacy for hardness (R² = 0.979; Adjusted R² = 0.944), dominated by WC and WC² effects, whereas impact energy was primarily influenced by nonlinear HWC terms with moderate predictability (R² = 0.882; Adjusted R² = 0.686). A feed-forward ANN (2–10–2) provided higher predictive accuracy (R² = 0.97 for hardness; R² = 0.99 for impact energy) compared to RSM. ANN was embedded into a desirability-based GA optimizer, yielding an optimal condition at WC = 200 A and HWC = 150 A, with predicted responses of 58.17 HRC and 35.55 J, with a high desirability (D) of 0.95. Microstructural and fractographic results supported enhanced weld uniformity and ductile fracture behavior.

Keywords:

TIG hot-wire welding, RSM–CCD, artificial neural network, genetic algorithm optimization

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