@article{Jiang2025, 
author = {Zihao Jiang and Caiyou Zeng and Zijin Chang and Ziqi Li and Yuan Zhao and Baoqiang Cong},
title = {Synergistic optimization of efficiency-microstructure-performance in wire-arc additive manufacturing of AZ31 magnesium alloy},
year = {2025},
journal = {Journal of Magnesium and Alloys},
volume = {13},
number = {11},
pages = {5571-5588},
keywords = {Wire-arc additive manufacturing, Magnesium alloys, Deposition efficiency, Microstructure, Process optimization},
url = {https://www.sciopen.com/article/10.1016/j.jma.2025.04.026},
doi = {10.1016/j.jma.2025.04.026},
abstract = {In wire arc additive manufacturing (WAAM), a trade-off exists among deposition efficiency, microstructure, and mechanical properties. Addressing this challenge, this work proposes an innovative multi-objective optimization framework tailored for WAAM of AZ31 magnesium alloy components, which integrates deposition efficiency and microstructure as coupled objectives and is resolved through the NSGA-II algorithm. The proposed framework employs quadratic regression to correlate process parameters with deposition efficiency through geometric morphology mediation, while addressing uncertainties in WAAM by integrating theoretical insights with data-driven stacked ensemble learning for grain size prediction, establishing the hybrid physics-informed data method for WAAM microstructure prediction. The optimized process achieved a deposition rate of 6257 mm³/min, with effective width and average layer height maintained at 10.1 mm and 4.13 mm, respectively. Microstructural optimization produced a fine, uniform, fully equiaxed grain structure with an average grain size of 38 μm. These findings underscore the significant industrial potential of intelligent optimization strategies in WAAM for manufacturing lightweight, high-performance components in aerospace and transportation sectors.}
}