[HTML][HTML] Research and application of machine learning for additive manufacturing

J Qin, F Hu, Y Liu, P Witherell, CCL Wang… - Additive …, 2022 - Elsevier
Additive manufacturing (AM) is poised to bring a revolution due to its unique production
paradigm. It offers the prospect of mass customization, flexible production, on-demand and …

Material machine learning for alloys: Applications, challenges and perspectives

X Liu, P Xu, J Zhao, W Lu, M Li, G Wang - Journal of Alloys and Compounds, 2022 - Elsevier
Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …

A systematic literature review on recent trends of machine learning applications in additive manufacturing

MD Xames, FK Torsha, F Sarwar - Journal of Intelligent Manufacturing, 2023 - Springer
Additive manufacturing (AM) offers the advantage of producing complex parts more
efficiently and in a lesser production cycle time as compared to conventional subtractive …

Multifidelity physics-constrained neural networks with minimax architecture

D Liu, P Pusarla, Y Wang - … of Computing and …, 2023 - asmedigitalcollection.asme.org
Data sparsity is still the main challenge to apply machine learning models to solve complex
scientific and engineering problems. The root cause is the “curse of dimensionality” in …

Directed Energy Deposition via Artificial Intelligence‐Enabled Approaches

U Chadha, SK Selvaraj, AS Lamsal, Y Maddini… - …, 2022 - Wiley Online Library
Additive manufacturing (AM) has been gaining pace, replacing traditional manufacturing
methods. Moreover, artificial intelligence and machine learning implementation has …