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Transfer learning of neural network based process models in Direct Metal Deposition

https://doi.org/10.1016/j.procir.2022.05.076
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781119513957.ch12openalex-snapshot
Recent Advances Toward the Industrialization of Metal Additive Manufacturing
inferredcites →https://doi.org/10.1007/s00170-020-05569-3openalex-snapshot
A review on in situ monitoring technology for directed energy deposition of metals
inferredcites →https://doi.org/10.1007/s10514-022-10034-zopenalex-snapshot
Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
inferredcites →https://doi.org/10.1016/j.addma.2018.05.017openalex-snapshot
A physical modeling and predictive simulation of the laser cladding process
inferredcites →https://doi.org/10.1016/j.addma.2019.03.017openalex-snapshot
Vision-based inspection system for cladding height measurement in Direct Energy Deposition (DED)
inferredcites →https://doi.org/10.1016/j.addma.2020.101692openalex-snapshot
Prediction of melt pool temperature in directed energy deposition using machine learning
inferredcites →https://doi.org/10.1016/j.cirp.2017.05.011openalex-snapshot
Laser based additive manufacturing in industry and academia
inferredcites →https://doi.org/10.1016/j.cossms.2016.12.001openalex-snapshot
Modeling of additive manufacturing processes for metals: Challenges and opportunities
inferredcites →https://doi.org/10.1016/j.optlaseng.2018.09.012openalex-snapshot
Monitoring of laser metal deposition height by means of coaxial laser triangulation
inferredcites →https://doi.org/10.1016/j.procir.2020.01.191openalex-snapshot
3D vision system integration on Additive Manufacturing machine for in-line part inspection
inferredcites →https://doi.org/10.1109/jproc.2020.3004555openalex-snapshot
A Comprehensive Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/lra.2018.2851792openalex-snapshot
Multisensor Data Fusion for Additive Manufacturing Process Control
inferredcites →https://doi.org/10.1115/1.4037235openalex-snapshot
An Extended Lumped-Parameter Model of Melt–Pool Geometry to Predict Part Height for Directed Energy Deposition
inferredcites →https://doi.org/10.3390/ma14020337openalex-snapshot
Model-Based Feedforward Control of Part Height in Directed Energy Deposition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4496911openalex-extracted
inferred← citeshttps://doi.org/10.1007/s00170-024-14543-2openalex-snapshot
A review of machine learning in additive manufacturing: design and process
inferred← citeshttps://doi.org/10.1007/s00170-024-14696-0openalex-snapshot
A dimensionless group-incorporating artificial neural network (DI-ANN) model for single-track depth prediction of SS316L for laser-directed energy deposition (L-DED)
inferred← citeshttps://doi.org/10.1007/s00170-024-14982-xopenalex-snapshot
Laser metal deposition as repair technology for Inconel 718
inferred← citeshttps://doi.org/10.1007/s10845-024-02402-6openalex-snapshot
Selecting subsets of source data for transfer learning with applications in metal additive manufacturing
inferred← citeshttps://doi.org/10.1016/j.addma.2022.103357openalex-snapshot
Review of transfer learning in modeling additive manufacturing processes
inferred← citeshttps://doi.org/10.1016/j.addma.2025.104905openalex-snapshot
Intelligent monitoring of porosity in laser melting deposition based on deep transfer learning
inferred← citeshttps://doi.org/10.1016/j.aei.2026.104962openalex-snapshot
Applications, challenges, recommendations, and future directions of additive manufacturing with transfer learning
inferred← citeshttps://doi.org/10.1016/j.cirpj.2023.06.014openalex-snapshot
A model-based approach to reduce kinematics-related overfill in robot-guided Laser Directed Energy Deposition
inferred← citeshttps://doi.org/10.1016/j.ijlmm.2025.04.001openalex-snapshot
Deep learning-based applications in metal additive manufacturing processes: Challenges and opportunities–A review
inferred← citeshttps://doi.org/10.1016/j.jmsy.2024.04.021openalex-snapshot
Leveraging small-scale datasets for additive manufacturing process modeling and part certification: Current practice and remaining gaps
inferred← citeshttps://doi.org/10.1016/j.optlastec.2026.114946openalex-snapshot
From physics to intelligence: a review of AI-driven modeling strategies in laser direct energy deposition additive manufacturing
inferred← citeshttps://doi.org/10.1016/j.rcim.2024.102779openalex-snapshot
Privacy-preserving federated transfer learning for defect identification from highly imbalanced image data in additive manufacturing
inferred← citeshttps://doi.org/10.1088/1758-5090/ade62fopenalex-snapshot
Leveraging transfer learning for efficient bioprinting
inferred← citeshttps://doi.org/10.1109/access.2023.3335130openalex-snapshot
In-Situ Process Monitoring and Defects Detection Based on Geometrical Topography With Streaming Point Cloud Processing in Directed Energy Deposition
inferred← citeshttps://doi.org/10.3390/app122110962openalex-snapshot
Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems
inferred← citeshttps://doi.org/10.3390/met15090966openalex-snapshot
A Review of Machine Learning Applications on Direct Energy Deposition Additive Manufacturing—A Trend Study
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