Machine Learning-Based Forming Quality Prediction and Multi-objective Optimization of Aluminum Alloy Thin-Walled Components in Wire Arc Additive Manufacturing

Abstract:

Wire arc additive manufacturing (WAAM) holds significant application value in the aerospace field, but the instability of heat input leads to prominent issues such as poor geometric conformity and numerous internal defects in aluminum alloy thin-walled components. To address the restrictions of traditional methods in multi-physics coupling optimization, this study proposed a data-driven solution by constructing a dataset of process parameters (current, scanning speed and wire feed rate) and forming quality (path/interlayer wall thickness consistency and porosity). A back propagation (BP) neural network model was established and optimized using the genetic algorithm (GA), combined with the non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization. The results show that the optimized GA-BP model significantly improves the prediction accuracy of path wall thickness consistency and porosity, but its optimization effect on interlayer wall thickness consistency prediction is restricted. Four types of optimization strategies are proposed based on the 50 Pareto solution sets obtained through NSGA-II, and validation tests indicate the model prediction error of 8.89%, accurately achieving the collaborative optimization of forming quality indicators.