A review on the enhancement of failure mechanisms modeling in additively manufactured structures by machine learning

M Awd, L Saeed, F Walther - Engineering Failure Analysis, 2023 - Elsevier
This review discusses the feasibility of using microstructure-and defect-sensitive models to
predict the fatigue behavior of additively generated materials through a non-exclusive …

A comprehensive review of recent advances in laser powder bed fusion characteristics modeling: metallurgical and defects

SF Nabavi, H Dalir, A Farshidianfar - The International Journal of …, 2024 - Springer
This comprehensive review explores recent advancements in laser powder bed fusion
(LPBF) modeling, with a particular focus on metallurgical, temperature, and defect aspects …

[HTML][HTML] Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and …

H Liu, C Gobert, K Ferguson, B Abranovic, H Chen… - Additive …, 2024 - Elsevier
With a growing demand for high-quality fabrication, the interest in real-time process and
defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers …

[HTML][HTML] Anomaly detection by X-ray tomography and probabilistic fatigue assessment of aluminum brackets manufactured by PBF-LB

L Rusnati, M Yosifov, S Senck, R Hubmann, S Beretta - Materials & Design, 2024 - Elsevier
The assessment of safety-critical components for fatigue applications is a key requirement
for metal additive manufacturing (AM) applications. Material anomalies play a relevant role …

[HTML][HTML] Fatigue-based process window for laser beam powder bed fusion additive manufacturing

T Reddy, A Ngo, JP Miner, C Gobert, JL Beuth… - International Journal of …, 2024 - Elsevier
Processing defects remain the primary cause for fatigue failure of laser beam powder bed
fusion (PBF-LB) produced components. Accordingly, process mapping methodologies have …

Inference of Highly Time-resolved Melt Pool Visual Characteristics in Laser Powder Bed Fusion from Acoustic and Thermal Emission Data

H Liu, C Gobert, K Ferguson, B Abranovic… - arXiv preprint arXiv …, 2023 - arxiv.org
With a growing demand for high-quality fabrication, the interest in real-time monitoring of
laser powder bed fusion (LPBF) processes has increased, leading manufacturers to …

Effect of support structures and surface angles on near-surface porosity in laser powder bed fusion

CL Smithson, T Davis, TW Nelson, NB Crane - Journal of Manufacturing …, 2023 - Elsevier
Additive manufacturing (AM) provides tremendous design freedom, but also introduces
many sources of defects. Use of AM in safety-critical parts requires an excellent …

Expediting structure–property analyses using variational autoencoders with regression

WF Templeton, JP Miner, A Ngo, L Fitzwater… - Computational Materials …, 2024 - Elsevier
We present a machine learning approach that expedites structure–property analysis in
materials, bypassing traditional feature extraction and exploratory data analysis techniques …

Statistical analysis to assess porosity equivalence with uncertainty across additively manufactured parts for fatigue applications

JP Miner, SP Narra - arXiv preprint arXiv:2411.03401, 2024 - arxiv.org
Previous work on fatigue prediction in Powder Bed Fusion-Laser Beam has shown that the
estimate of the largest pore size within the stressed volume is correlated with the resulting …