In-situ full-field mapping of melt flow dynamics in laser metal additive manufacturing
By Qilin Guo, Cang Zhao, Minglei Qu, Lianghua Xiong, S. Hojjatzadeh, Luis I. Escano, N. Parab, K. Fezzaa, T. Sun, Lianyi Chen. 2020
235 citations recorded by the source corpus.
Abstract Melt flow plays a critical role in laser metal additive manufacturing, yet the melt flow behavior within the melt pool has never been explicitly presented. Here, we report in-situ characterization of melt-flow dynamics in every location of the entire melt pool in laser metal additive manufacturing by populous and uniformly dispersed micro-tracers through in-situ high-resolution synchrotron x-ray imaging. The location-specific flow patterns in different regions of the melt pool are revealed and quantified under both conduction-mode and depression-mode melting. The physical processes at different locations in the melt pool are identified. The full-field melt-flow mapping approach reported here opens the way to study the detailed melt-flow dynamics under real additive manufacturing conditions. The results obtained provide crucial insights into laser additive manufacturing processes and are critical for developing reliable high-fidelity computational models.
Subfields: Automotive Engineering, Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/f84fce8b7335ead4a5d4c457df6cc03c3e190d2b
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Citations in the corpus
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (2020, background): “For the past several years, predictive computational models, in conjunction with in-situ and ex-situ measurements and monitoring [1–3], have been playing an indispensable role in enhancing the understanding of the processstructure-property relationship in metal AM.”
- Mechanism of keyhole pore formation in metal additive manufacturing (2021, methodology): “1a) like the X-ray imaging results by Guo et al.31.”