A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Laser powder bed fusion (LPBF) as one of the most promising additive manufacturing (AM)
technologies, has been widely used to produce metal parts and applied in fields such as …
technologies, has been widely used to produce metal parts and applied in fields such as …
[HTML][HTML] Influence of mean stress and building orientation on the fatigue properties of sub-unital thin-strut miniaturized Ti6Al4V specimens additively manufactured via …
Fatigue is a complex, localized phenomenon affecting lattice structures at the level of struts
and junctions. This study examines the fatigue properties of Laser-Powder Bed Fusion (L …
and junctions. This study examines the fatigue properties of Laser-Powder Bed Fusion (L …
[HTML][HTML] Machine learning-assisted extreme value statistics of anomalies in AlSi10Mg manufactured by L-PBF for robust fatigue strength predictions
Abstract Traditional Extreme Value Statistics (EVS) applied to block maxima sampled
anomalies of components produced by Laser-Powder Bed Fusion may produce important …
anomalies of components produced by Laser-Powder Bed Fusion may produce important …
Volumetric defect classification in Nano-resolution X-ray computed tomography images of laser powder bed fusion via deep learning
Additively manufactured (AMed) components often contain volumetric defects that
significantly impact mechanical and fatigue properties across various material systems …
significantly impact mechanical and fatigue properties across various material systems …
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder
bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms …
bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms …
[HTML][HTML] Deep Learning-Based Defects Detection in Keyhole TIG Welding with Enhanced Vision
X Zhang, S Zhao, M Wang - Materials, 2024 - mdpi.com
Keyhole tungsten inert gas (keyhole TIG) welding is renowned for its advanced efficiency,
necessitating a real-time defect detection method that integrates deep learning and …
necessitating a real-time defect detection method that integrates deep learning and …
Three-Dimensional X-Ray Computed Tomography Image Segmentation and Point Cloud Reconstruction for Internal Defect Identification in Laser Powder Bed Fused …
B Xu, H Ouidadi, NV Handel… - Journal of …, 2024 - asmedigitalcollection.asme.org
Defects shape, volume, and orientation all have a direct impact on the mechanical
properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to …
properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to …
Estimating Pore Location of PBF-LB/M Processes with Segmentation Models
HA Zhou, J Theunissen, M Kemmerling… - arXiv preprint arXiv …, 2024 - arxiv.org
Reliably manufacturing defect free products is still an open challenge for Laser Powder Bed
Fusion processes. Particularly, pores that occur frequently have a negative impact on …
Fusion processes. Particularly, pores that occur frequently have a negative impact on …