A method for melt pool state monitoring in laser-based direct energy deposition based on DenseNet
出版年份 2022 全文链接
标题
A method for melt pool state monitoring in laser-based direct energy deposition based on DenseNet
作者
关键词
Laser-based direct energy deposition, Melt pool state, Deep learning, Online monitoring
出版物
MEASUREMENT
Volume 195, Issue -, Pages 111146
出版商
Elsevier BV
发表日期
2022-04-09
DOI
10.1016/j.measurement.2022.111146
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Failure classification of porous additively manufactured parts using Deep Learning
- (2022) Kyle L. Johnson et al. COMPUTATIONAL MATERIALS SCIENCE
- Directed energy deposition (DED) additive manufacturing: Physical characteristics, defects, challenges and applications
- (2021) David Svetlizky et al. Materials Today
- Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks
- (2021) Brendan P. Croom et al. MECHANICS OF MATERIALS
- Process performance evaluation and classification via in-situ melt pool monitoring in directed energy deposition
- (2021) Deniz Sera Ertay et al. CIRP Journal of Manufacturing Science and Technology
- Real–time prediction of penetration depths of laser surface melting based on coaxial visual monitoring
- (2020) Zi-jue Tang et al. OPTICS AND LASERS IN ENGINEERING
- A review on in situ monitoring technology for directed energy deposition of metals
- (2020) Zi-jue Tang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- An improved methodology of melt pool monitoring of direct energy deposition processes
- (2020) Robert Sampson et al. OPTICS AND LASER TECHNOLOGY
- Effect of Process Parameters on Defects, Melt Pool Shape, Microstructure, and Tensile Behavior of 316L Stainless Steel Produced by Selective Laser Melting
- (2020) Hua-Zhen Jiang et al. Acta Metallurgica Sinica-English Letters
- Metal additive manufacturing: Technology, metallurgy and modelling
- (2020) Shaun Cooke et al. Journal of Manufacturing Processes
- Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks
- (2019) Yingjie Zhang et al. IEEE Transactions on Industrial Informatics
- Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images
- (2018) Masoumeh Aminzadeh et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Melt pool sensing and size analysis in laser powder-bed metal additive manufacturing
- (2018) Bo Cheng et al. Journal of Manufacturing Processes
- Process monitoring and inspection systems in metal additive manufacturing: Status and applications
- (2017) Zhong Yang Chua et al. International Journal of Precision Engineering and Manufacturing-Green Technology
- Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones
- (2016) Saad A. Khairallah et al. ACTA MATERIALIA
- On the role of melt flow into the surface structure and porosity development during selective laser melting
- (2015) Chunlei Qiu et al. ACTA MATERIALIA
- Deep learning
- (2015) Yann LeCun et al. NATURE
- A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
- (2014) Gustavo Tapia et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool
- (2013) N. Chandrasekhar et al. JOURNAL OF INTELLIGENT MANUFACTURING
- Melt Pool Temperature Control for Laser Metal Deposition Processes—Part I: Online Temperature Control
- (2010) Lie Tang et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
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