Optimization of AA2024 Aluminum Alloy Welding Processes in Aerospace Manufacturing

Thesis Abstract

The increasing demand for lightweight, high-performance components in the aerospace industry necessitates the optimization of welding processes for aluminum alloys, with AA2024 being one of the most widely used due to its high strength-to-weight ratio and excellent fatigue resistance. However, traditional welding techniques often result in defects such as cracks, porosity, and inconsistent mechanical properties, which compromise the integrity and safety of aerospace structures. This study aims to systematically optimize key parameters of the gas tungsten arc welding (GTAW) process for AA2024 aluminum alloy to enhance weld quality, strength, and durability while minimizing defects, thereby supporting the manufacturing of safer and more efficient aerospace components. The specific objectives include identifying critical welding parameters affecting weld quality, developing empirical models to predict weld performance based on parameter settings, and establishing optimal process conditions through experimental and analytical means. The research adopts an experimental design incorporating a factorial processing framework, utilizing a parametric approach to examine the influence of welding current, arc voltage, welding speed, and shielding gas flow rate on weld characteristics. The population comprises AA2024 aluminum alloy sheets supplied by a leading aerospace manufacturer, with a sample size of 120 specimens systematically prepared for welding trials based on Taguchi’s L16 orthogonal array to ensure comprehensive coverage of parameter combinations. Data collection involves both destructive and non-destructive testing methods, including tensile testing, hardness measurements, and radiographic inspections to evaluate mechanical properties and defect prevalence. Additionally, microstructural analysis is conducted via optical and scanning electron microscopy (SEM) to elucidate metallurgical features associated with different process conditions. Data analysis employs analysis of variance (ANOVA) to determine the significance of each welding parameter, alongside regression analysis to develop predictive models for weld strength, hardness, and defect occurrence. Response surface methodology (RSM) facilitates the visualization of the relationship between process variables and weld quality, guiding the identification of optimal parameter combinations. The study draws on the Theory of Constraints and the Fishbone (Ishikawa) diagram to systematically identify and address factors limiting weld quality, aligning empirical findings with theoretical insights into process controls. Reliability testing and validation are performed using confirmatory experiments conducted under the identified optimal conditions to ensure robustness and repeatability of the results. Expected findings indicate that optimal welding parameters can significantly improve mechanical properties and reduce defect rates in AA2024 welds, with a notable contribution of welding current and shielding gas flow rate to the overall weld quality. The empirical models developed will offer precise insights into the process-structure-property relationship, enabling manufacturers to adopt scientifically grounded process settings. It is anticipated that the microstructural characterization will reveal improved grain refinement and reduced porosity at optimal conditions, correlating with enhanced tensile strength and hardness. This study makes a substantive contribution to the existing body of knowledge by providing an integrated, data-driven framework for optimizing welding parameters specific to AA2024 in aerospace manufacturing, bridging laboratory research with industrial application. The findings are expected to facilitate the development of standardized welding procedures that enhance both safety and productivity in the aerospace sector. The main conclusion underscores the importance of precise process control and empirical modeling in achieving defect-free, high-strength welds. Recommendations include the adoption of the proposed optimized parameters in industrial settings, further investigation into the effects of post-weld heat treatments, and extending the research framework to other aerospace-grade aluminum alloys. Future research directions suggest exploring real-time monitoring techniques such as acoustic emission and machine learning algorithms for adaptive process control, to further refine welding performance in dynamic manufacturing environments.