Abstract
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Qianru Wu, Yichen Lu, Wenlin Han, Rongxiang Zhang, Zixiang Li, Wenlai Tang, Jiquan Yang
Abstract
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Multi-source optical signal monitoring system for spatter-induced lack-of-fusion defects produced in laser powder bed fusion
10.1016/j.optlastec.2026.115114 · 2026
In-situ monitoring of porosity based on static and dynamic molten pool features in laser powder bed fusion
10.1016/j.optlastec.2025.112872 · 2025
Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: defect formation, geometric precision, and process mapping
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Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing
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Superior strength-ductility synergy in laser-additive manufactured Ti60 alloy via carbon-driven α2 phase refinement and pyramidal 〈c+a〉 dislocation activity
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Application of machine vision for in-situ monitoring in metal additive manufacturing: a review
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Investigation on coaxial visual characteristics of molten pool in laser-based directed energy deposition of AISI 316L steel
10.1016/j.jmatprotec.2020.116996 · 2021
Process monitoring by deep neural networks in directed energy deposition: CNN-based detection, segmentation, and statistical analysis of melt pools
10.1016/j.rcim.2023.102710 · 2024
Provenance
crossref
Confidence 100%
ror
Confidence 99%
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datacite
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Online cladding quality assessment based on YOLOv8-RF model using molten pool images
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Convolutional neural network for heat source parameter recognition of TC4 laser cladding driven by melt pool morphology and contour deformation field data
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Study on the visualization of laser cladding molten pool flow field based on attention mechanism
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Motion feature based melt pool monitoring for selective laser melting process
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Interplay mechanism between molten pool, spatter, vapor and melt defects in laser beam powder bed fusion via in-situ schlieren monitoring and deep learning methods
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Acousto-optic signal-based in-situ measurements supporting part quality improvement in additive manufacturing
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Unraveling the potential of multi-sensor fusion of acoustic signals and melt pool geometric images towards defect identification in additive manufacturing
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Local defects prediction in laser additive manufacturing via multisensor monitoring strategy and multi-feature fusion convolutional neural network
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Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition
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Online defect detection method in laser powder bed fusion process based on spatiotemporal propagation characteristics of melt pool
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In-situ melt pool characterization via thermal imaging for defect detection in directed energy deposition using vision transformers
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Prediction of melt pool width and layer height for laser directed energy deposition enabled by physics-driven temporal convolutional network
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A PINN–EWMA framework with uncertainty quantification for high-fidelity temperature field modeling and early defect warning in directed energy deposition of thin-walled structures
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A physics-aware autoregressive encoder–decoder for fast surrogate prediction of transient melt pool dynamics in laser welding and additive manufacturing
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Recent advances in machine learning for defects detection and prediction in laser cladding process
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AI-vision based real-time in-situ monitoring of the metallic deposition in μ-plasma transferred arc directed energy deposition process
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AI-vision based real-time in-situ monitoring of the metallic deposition in μ-plasma transferred arc directed energy deposition process
10.1016/j.jmapro.2026.05.060 · doi-reference
A physics-aware autoregressive encoder–decoder for fast surrogate prediction of transient melt pool dynamics in laser welding and additive manufacturing
10.1016/j.jmapro.2026.05.003 · doi-reference
Prediction of melt pool width and layer height for laser directed energy deposition enabled by physics-driven temporal convolutional network
10.1016/j.jmsy.2023.06.002 · doi-reference
Online monitoring of local defects in robotic laser additive manufacturing process based on a dynamic mapping strategy and multibranch fusion convolutional neural network
10.1016/j.jmsy.2023.10.005 · doi-reference
Process mapping and anomaly detection in laser wire directed energy deposition additive manufacturing using in-situ imaging and process-aware machine learning
10.1016/j.matdes.2024.113281 · doi-reference
In-situ melt pool characterization via thermal imaging for defect detection in directed energy deposition using vision transformers
10.1016/j.jmapro.2025.03.123 · doi-reference
Research on rapid prediction method of laser cladding deposited layer state based on molten pool texture sequence
10.1016/j.optlastec.2024.111857 · doi-reference
Online defect detection method in laser powder bed fusion process based on spatiotemporal propagation characteristics of melt pool
10.1016/j.engappai.2026.114591 · doi-reference
Spatiotemporal analysis of powder bed fusion melt pool monitoring videos using deep learning
10.1007/s10845-024-02355-w · doi-reference
A monitoring method for local defects in laser additive manufacturing process based on molten pool spatiotemporal information fusion
10.1016/j.jmapro.2024.12.048 · doi-reference
A recurrent neural network-based monitoring system using time-sequential molten pool images in wire arc directed energy deposition
10.1016/j.ymssp.2025.112733 · doi-reference
Deep learning-based data fusion method for in situ porosity detection in laser-based additive manufacturing
10.1115/1.4048957 · doi-reference
Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition
10.1016/j.rcim.2023.102581 · doi-reference
Local defects prediction in laser additive manufacturing via multisensor monitoring strategy and multi-feature fusion convolutional neural network
10.1016/j.optlastec.2025.113005 · doi-reference
Unraveling the potential of multi-sensor fusion of acoustic signals and melt pool geometric images towards defect identification in additive manufacturing
10.1016/j.jmapro.2025.12.006 · doi-reference
Acousto-optic signal-based in-situ measurements supporting part quality improvement in additive manufacturing
10.1016/j.measurement.2024.115786 · doi-reference
Motion feature based melt pool monitoring for selective laser melting process
10.1016/j.jmatprotec.2022.117523 · doi-reference
Study on the visualization of laser cladding molten pool flow field based on attention mechanism
10.1016/j.jmapro.2024.12.034 · doi-reference
Convolutional neural network for heat source parameter recognition of TC4 laser cladding driven by melt pool morphology and contour deformation field data
10.1016/j.optlastec.2025.112874 · doi-reference
Online cladding quality assessment based on YOLOv8-RF model using molten pool images
10.1016/j.jmapro.2025.10.102 · doi-reference
Process monitoring by deep neural networks in directed energy deposition: CNN-based detection, segmentation, and statistical analysis of melt pools
10.1016/j.rcim.2023.102710 · doi-reference
Detecting voids in 3D printing using melt pool time series data
10.1007/s10845-020-01694-8 · doi-reference
Investigation on coaxial visual characteristics of molten pool in laser-based directed energy deposition of AISI 316L steel
10.1016/j.jmatprotec.2020.116996 · doi-reference
Multi-physics melt pool modeling and process optimization for laser direct energy deposition of Nb-based refractory C103: defect formation, geometric precision, and process mapping
10.1016/j.jmapro.2025.09.082 · doi-reference
In-situ monitoring of porosity based on static and dynamic molten pool features in laser powder bed fusion
10.1016/j.optlastec.2025.112872 · doi-reference
Multi-source optical signal monitoring system for spatter-induced lack-of-fusion defects produced in laser powder bed fusion
10.1016/j.optlastec.2026.115114 · doi-reference