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A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

机译：基于深度学习的缺陷检测模型，用于使用原位热选监控的激光粉床融合

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Hermann Baumgartl; Josef Tomas; Ricardo Buettner; Markus Merkel;
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1. In-Situ defect detection and monitoring for laser powder bed fusion using a multi-sensor build platform [J] . Clemens Maucher, Kim Torben Werkle, Hans-Christian M?hring Procedia CIRP . 2021,第a期

机译：使用多传感器构建平台的激光粉床融合的原位缺陷检测和监控
2. In-situ characterization of laser-powder interaction and cooling rates through high-speed imaging of powder bed fusion additive manufacturing [J] . Bertoli Umberto Scipioni, Guss Gabe, Wu Sheldon, Materials & design . 2017,第deca期

机译：通过粉末床熔融增材制造的高速成像原位表征激光与粉末的相互作用和冷却速率
3. Locally Adaptive Statistical Background Modeling With Deep Learning-Based False Positive Rejection for Defect Detection in Semiconductor Units [J] . Haddad Bashar M., Dodge Samuel F., Karam Lina J., IEEE Transactions on Semiconductor Manufacturing . 2020,第3期

机译：基于深度学习的虚假阳性抑制的本地自适应统计背景模型，用于半导体单元缺陷检测
4. Meso-scale modeling of formation of balling defect in laser-powder bed fusion additive manufacturing [C] . Yi Li, Wei Zhang, Yousub Lee International Conference on Trends in Welding Research . 2016

机译：激光粉末融合添加剂制造中缺陷缺陷形成的中学规模建模
5. Instrumented Build Plate for in-situ Stress Monitoring and Crack Detection during the Laser Powder Bed Fusion Additive Manufacturing Process [D] . ?Ludwig, Samuel 2020

机译：仪表板构建为激光粉末床聚变添加制造过程中地应力监测和裂缝检测
6. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study [O] . Xiangjun Wu, Hui Hui, Meng Niu, -1

机译：基于深度学习的多视图融合模型用于筛选2019年新型冠状病毒性肺炎：一项多中心研究
7. Influence of Different Alloying Strategies on the Mechanical Behavior of Tool Steel Produced by Laser-Powder Bed Fusion [O] . Abootorab Baqerzadeh Chehreh, Anna Strauch, Felix Großwendt, 2021

机译：不同合金策略对激光粉床融合生产工具钢力学行为的影响

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

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