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Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions

Donghyun Ra et al.

Journal of Manufacturing Systems2026https://doi.org/10.1016/j.jmsy.2026.01.002article
AJG 1ABDC B
Weight
0.44

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3 citations

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https://doi.org/https://doi.org/10.1016/j.jmsy.2026.01.002

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@article{donghyun2026,
  title        = {{Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions}},
  author       = {Donghyun Ra et al.},
  journal      = {Journal of Manufacturing Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jmsy.2026.01.002},
}

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Physics-informed machine learning across manufacturing processes: Recent advances, challenges, and directions

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.