Hybrid ANN-GA and RSM-GA Models for Optimizing GMAW Parameters to Enhance Impact Strength of Dissimilar Aluminum Alloys

Yodprem Pookamnerd; Thanatep  Phatungthane; Chuthong Summatta

Authors

Yodprem Pookamnerd Nakhon Phanom University, Thailand
Thanatep Phatungthane Nakhon Phanom University, Thailand
Chuthong Summatta Nakhon Phanom University, Thailand

Keywords:

welding optimization, ANN-GA, RSM-GA, dissimilar alloys, GMAW, impact strength

Abstract

Optimizing welding parameters is essential for achieving superior mechanical performance, particularly in joining dissimilar aluminum alloys such as AA6061 and AA7075 using Gas Metal Arc Welding (GMAW). Unlike previous studies that primarily rely on single-model frameworks or conventional optimization techniques, this research uniquely integrates both ANN-GA and RSM-GA models to comprehensively evaluate and compare their performance on dissimilar aluminum alloy welds (AA6061–AA7075), offering a dual-model perspective rarely explored in the literature. This study employs a comprehensive approach integrating Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to optimize parameters for enhanced impact strength. The Response Surface Methodology (RSM) with GA is also applied to analyze parameter interactions. Materials, including AA6061, AA7075, and ER5356 filler wire, were selected for their chemical compositions, balancing strength, corrosion resistance, and weldability. Using robotic GMAW ensured precision and repeatability, while Charpy impact testing evaluated joint toughness. The ANN-GA model identified optimal parameters—welding current of 143.08 A, speed of 2.61 mm/s, and wire feed rate of 3.86 m/min—achieving an impact energy of 20.00 J. Similarly, the RSM-GA framework yielded comparable results with parameters of 148.55 A, 2.52 mm/s, and 3.68 m/min. The comparative analysis highlighted the ANN-GA model's superior predictive accuracy (RMSE = 0.79815, MAPE = 3.7979), while RSM-GA provided insights into parameter trends. The outcomes provide a robust foundation for improving weld performance and optimizing parameters in industrial applications, demonstrating the practical value and adaptability of AI-driven welding models. The findings advocate integrating ANN and RSM models to enhance parameter optimization, offering practical applications in aerospace, automotive, and structural engineering industries.

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