Differential Evolution-Optimized Multi-Output Support Vector Regression-Based Prediction of Weld Bead Morphology in Wire-Fed Laser-Arc Directed Energy Deposition of 2319 Aluminum Alloy

作者：Li R, Hui Ma, Kui Zeng, Haoyuan Suo, Chenyu Li, Youheng Fu, Mingbo Zhang, Maoyuan Zhang, Xuewei Fang · 发表于：Additive Manufacturing Frontiers · 年份：2025 · DOI：10.1016/j.amf.2025.200203 · 被引用次数：16 · 研究领域：Additive Manufacturing Materials and Processes、Welding Techniques and Residual Stresses、Advanced Welding Techniques Analysis

Wire-fed laser-arc directed energy deposition (Wire-fed LA-DED) technology improves production speed while maintaining high quality and is particularly suited for manufacturing large, complex aluminum or titanium alloy components. The geometry of the weld bead (height and width) is influenced by multiple intricate parameters and variables during the manufacturing process. Accurately predicting the weld bead shape enables precise control over the surface flatness of the part, helping to prevent defects such as lack of fusion. This significantly reduces dimensional redundancy, enhances printing efficiency, and optimizes material usage. In this study, a quadratic regression prediction model for weld bead geometry was developed using the response surface methodology (RSM), with predictions generated through several machine learning models. These models included the backpropagation neural network (BPNN), support vector regression (SVR), multi-output support vector regression (MOSVR), extreme learning machine (ELM), and a differential evolution-optimized MOSVR (DE-MOSVR) model. Grid search and cross-validation techniques were utilized to identify the optimal parameters for each model to achieve the best predictive performance. A comparison of these models was conducted, followed by an evaluation of their generalization capabilities using an additional 20 sets of test data. The most accurate predictive model was selected based on a comprehensive assessment. The results showed that t...