研究论文
COMPUTATIONAL, DATA-DRIVEN, AND PHYSICS-INFORMED MACHINE LEARNING APPROACHES FOR MICROSTRUCTURE MODELING IN METAL ADDITIVE MANUFACTURING
D. Patel, Rahul Sharma, Y. B. Guo
来源Annual Reviews of Heat Transfer
年份2025
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工程Additive Manufacturing Materials and Processes
Additive Manufacturing and 3D Printing Technologies · Machine Learning in Materials Science
参考文献 144
Microstructural and texture development in direct laser fabricated IN718
被引 572Lakshmi Lavanya Parimi, G.A. Ravi, Daniel Clark · Materials Characterization · 2014
Grain morphology evolution behavior of titanium alloy components during laser melting deposition additive manufacturing
被引 529T. Wang, Ying Yang Zhu, S.Q. Zhang · Journal of Alloys and Compounds · 2015
Measurements and Monte Carlo simulation of grain growth in the heat-affected zone of Ti–6Al–4V welds
被引 136S Mishra · Acta Materialia · 2003
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引用本文 10
Cellular Automata and Phase-Field Modeling of Microstructure Evolution in Metal Additive Manufacturing: Recent Advances, Hybrid Frameworks, and Pathways to Predictive Control
被引 8Łukasz Łach · Metals · 2026
Physics-informed machine learning framework for grain size prediction in laser-processed 316L stainless steel: integration of experimental microstructure and surrogate data
被引 6Aswin Karkadakattil · Canadian Metallurgical Quarterly · 2025
Physics-Informed Neural Networks for Real-Time Control of Grid-Forming Inverters: Embedding Physical System Laws into Deep Learning Architectures
被引 3Sipokazi Mabuwa, Katleho Moloi · Energies · 2026
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