[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 …

Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review

Y Fu, ARJ Downey, L Yuan, T Zhang, A Pratt… - Journal of Manufacturing …, 2022‏ - Elsevier
Laser-based additive manufacturing (LBAM), a series of additive manufacturing
technologies, has unrivaled advantages due to its design freedom to manufacture complex …

[HTML][HTML] Metal vaporization and its influence during laser powder bed fusion process

J Liu, P Wen - Materials & Design, 2022‏ - Elsevier
Laser powder bed fusion (LPBF) is a key metal additive manufacturing process and has
attracted increasing attention both in academia and industry. An essential physical issue …

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

M Grasso, A Remani, A Dickins… - Measurement …, 2021‏ - iopscience.iop.org
The possibility of using a variety of sensor signals acquired during metal powder bed fusion
processes, to support part and process qualification and for the early detection of anomalies …

A review of spatter in laser powder bed fusion additive manufacturing: In situ detection, generation, effects, and countermeasures

Z Li, H Li, J Yin, Y Li, Z Nie, X Li, D You, K Guan… - Micromachines, 2022‏ - mdpi.com
Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed
fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the …

Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

Y Liu, L Guo, H Gao, Z You, Y Ye, B Zhang - Mechanical Systems and …, 2022‏ - Elsevier
Abstract Machine vision based condition monitoring and fault diagnosis of machine tools
(MVCMFD-MTs) is a vital technique of condition-based maintenance (CBM) in both metal …

Defect inspection technologies for additive manufacturing

Y Chen, X Peng, L Kong, G Dong… - … Journal of Extreme …, 2021‏ - iopscience.iop.org
Additive manufacturing (AM) technology is considered one of the most promising
manufacturing technologies in the aerospace and defense industries. However, AM …

Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects

Y Du, T Mukherjee, T DebRoy - Applied Materials Today, 2021‏ - Elsevier
In the past few decades, additive manufacturing has evolved for the one-step fabrication of
various complex, customized metallic components that cannot be easily and economically …

Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges

P Wang, Y Yang, NS Moghaddam - Journal of Manufacturing Processes, 2022‏ - Elsevier
In recent years, machine learning (ML) techniques have been extensively investigated to
strengthen the understanding of the complex process dynamics underlying metal additive …

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 …