Optimized GMAW parameters for enhancing mechanical properties of dissimilar AA6061 and AA7075 alloy welds using hybrid ANN-GA approach

Abstract

Connecting aluminum alloys AA6061 and AA7075 presents significant challenges due to differences in thermal behaviors and metallurgical characteristics, often causing issues like cracking or warping during welding. Gas Metal Arc Welding (GMAW) is commonly used for aluminum alloys, but optimizing welding parameters for high-quality joints remains complex. Traditional methods are often inefficient and inadequate in multi-objective scenarios. Recent advancements in artificial intelligence (AI) offer promising alternatives, but applying AI in GMAW optimization for dissimilar aluminum alloys is still in the early stages. This study uses a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to optimize GMAW parameters for these alloys, addressing gaps in traditional approaches. The ANN model, trained on experimental data, predicts tensile strength and hardness, while GA optimizes key welding parameters, including current, speed, and wire feed rate, to improve joint performance. The ANN-GA model achieved optimal settings, reaching a peak tensile strength of 237.47 MPa and maximum hardness of 98.72 HV, substantially enhancing the mechanical properties of the welded joints. These findings underscore the effectiveness of the hybrid ANN-GA approach in GMAW process optimization for dissimilar aluminum alloys. This study advances welding technology and establishes a strong framework for applying AI-driven optimization techniques to complex manufacturing processes. These insights provide valuable guidance for improving weld quality in industrial applications and pave the way for further integration of AI techniques to enhance welding performanc