Understanding melt pool characteristics in laser powder bed fusion: An overview of single- and multi-track melt pools for process optimization

融合 过程(计算) 微观结构 工艺工程 多孔性 材料科学 进程窗口 计算机科学 表征(材料科学) 冶金 纳米技术 工程类 复合材料 语言学 操作系统 哲学
作者
Jincheng Wang,Rui Zhu,Yujing Liu,Lai‐Chang Zhang
出处
期刊:Advanced powder materials [Elsevier]
卷期号:2 (4): 100137-100137 被引量:365
标识
DOI:10.1016/j.apmate.2023.100137
摘要

Laser powder bed fusion (LPBF) has made significant progress in producing solid and porous metal parts with complex shapes and geometries. However, LPBF produced parts often have defects (e.g., porosity, residual stress, and incomplete melting) that hinder its large-scale industrial commercialization. The LPBF process involves complex heat transfer and fluid flow, and the melt pool is a critical component of the process. The melt pool stability is a critical factor in determining the microstructure, mechanical properties, and corrosion resistance of LPBF produced metal parts. Furthermore, optimizing process parameters for new materials and designed structures is challenging due to the complexity of the LPBF process. This requires numerous trial-and-error cycles to minimize defects and enhance properties. This review examines the behavior of the melt pool during the LPBF process, including its effects and formation mechanisms. This article summarizes the experimental results and simulations of melt pool and identifies various factors that influence its behavior, which facilitates a better understanding of the melt pool's behavior during LPBF. This review aims to highlight key aspects of the investigation of melt pool tracks and microstructural characterization, with the goal of enhancing a better understanding of the relationship between alloy powder-process-microstructure-properties in LPBF from both single- and multi-melt pool track perspectives. By identifying the challenges and opportunities in investigating single- and multi-melt pool tracks, this review could contribute to the advancement of LPBF processes, optimal process window, and quality optimization, which ultimately improves accuracy in process parameters and efficiency in qualifying alloy powders.
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