In-situ visual monitoring for multi-scale defects detection in laser powder bed fusion
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (73)
Show source-provided references
1. Additive manufacturing: challenges, trends, and applications10.1177/1687814018822880 ↗
2. Gibson · 2021
3. Metal additive manufacturing: a review10.1007/s11665-014-0958-z ↗
4. Review of selective laser melting: materials and applications10.1063/1.4935926 ↗
5. Processing parameters in laser powder bed fusion metal additive manufacturing10.1016/j.matdes.2020.108762 ↗
6. Defects and anomalies in powder bed fusion metal additive manufacturing10.1016/j.cossms.2021.100974 ↗
7. Defect formation mechanisms in selective laser melting: a review10.1007/s10033-017-0121-5 ↗
8. In-situ monitoring in L-PBF: opportunities and challenges10.1016/j.procir.2020.09.151 ↗
9. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing10.1016/j.matdes.2016.01.099 ↗
10. Process defects and in situ monitoring methods in metal powder bed fusion: a review10.1088/1361-6501/aa5c4f ↗
11. Layerwise Monitoring of the Selective Laser Melting Process by Thermography10.1016/j.phpro.2014.08.097 ↗
12. S. Moylan, E. Whitenton, B. Lane, J. Slotwinski, Infrared thermography for laser-based powder bed fusion additive manufacturing processes, in: Baltimore, Maryland, USA, 2014: pp. 1191–1196. https://doi.org/10.1063/1.4864956.10.1063/1.4864956 ↗
13. Consolidation characteristics of ferrous-based metal powder in additive manufacturing10.1299/jamdsm.2014jamdsm0009 ↗
14. Analysis of laser–melt pool–powder bed interaction during the selective laser melting of a stainless steel10.2351/1.4983259 ↗
15. Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data
16. Transient laser energy absorption, co-axial melt pool monitoring, and relationship to melt pool morphology
17. Laser ultrasonic inspection of additive manufactured components10.1007/s00170-018-3046-y ↗
18. Identification and evaluation of defects in selective laser melted 316L stainless steel parts via in-situ monitoring and micro computed tomography
19. Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion10.1126/science.add4667 ↗
20. In-situ monitoring of laser-based powder bed fusion using fringe projection
21. In situ defect detection in selective laser melting via full-field infrared thermography
22. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging
23. Detection of elevated regions in surface images from laser beam melting processes
24. Online quality control of selective laser melting
25. Error detection in laser beam melting systems by high resolution imaging
26. Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges10.1007/s10845-022-01972-7 ↗
27. Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: a review10.3390/app112411910 ↗
28. Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing10.1080/17452759.2021.1944229 ↗
29. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images10.1007/s10845-018-1412-0 ↗
30. Machine learning-based image processing for on-line defect recognition in additive manufacturing10.1016/j.cirp.2019.03.021 ↗
31. In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting10.1007/s10845-021-01829-5 ↗
32. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process
33. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm
34. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: a machine-agnostic algorithm for real-time pixel-wise semantic segmentation
35. A layer-wise multi-defect detection system for powder bed monitoring: lighting strategy for imaging, adaptive segmentation and classification10.1016/j.matdes.2021.110035 ↗
36. Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion10.1080/17452759.2022.2129396 ↗
37. Uncertainty-driven trustworthy defect detection for high-resolution powder bed images in selective laser melting10.1016/j.jmsy.2023.11.006 ↗
38. Flaw detection in powder bed fusion using optical imaging
39. Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion
40. Automated layerwise detection of geometrical distortions in laser powder bed fusion
41. Deep learning of variant geometry in layerwise imaging profiles for additive manufacturing quality control10.1115/1.4044420 ↗
42. D. Cannizzaro, A.G. Varrella, S. Paradiso, R. Sampieri, E. Macii, E. Patti, S.D. Cataldo, Image analytics and machine learning for in-situ defects detection in Additive Manufacturing, in: 2021 Des. Autom. Test Eur. Conf. Exhib. DATE, IEEE, Grenoble, France, 2021: pp. 603–608. https://doi.org/10.23919/DATE51398.2021.9474175.10.23919/DATE51398.2021.9474175 ↗
43. Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing10.1016/j.jmsy.2022.06.010 ↗
44. Powder bed monitoring via digital image analysis in additive manufacturing
45. LabelMe: a database and web-based tool for image annotation10.1007/s11263-007-0090-8 ↗
46. Physics of humping formation in laser powder bed fusion10.1016/j.ijheatmasstransfer.2019.119172 ↗
47. Defect detection in welding radiographic images based on semantic segmentation methods10.1016/j.measurement.2021.110569 ↗
48. A systematic review on data scarcity problem in deep learning: solution and applications10.1145/3502287 ↗
49. Real-time identification of molten pool and keyhole using a deep learning-based semantic segmentation approach in penetration status monitoring10.1016/j.jmapro.2022.02.058 ↗
50. Real-time recognition of arc weld pool using image segmentation network10.1016/j.jmapro.2021.10.019 ↗
51. The machined surface defect detection of improved superpixel segmentation and two-level region aggregation based on machine vision10.1016/j.jmapro.2022.05.038 ↗
52. LSA-Net: location and shape attention network for automatic surface defect segmentation10.1016/j.jmapro.2023.05.001 ↗
53. Fully Convolutional Networks for Semantic Segmentation10.1109/TPAMI.2016.2572683 ↗
54. U-Net: Convolutional Networks for Biomedical Image Segmentation
55. L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, (2018). 〈http://arxiv.org/abs/1802.02611〉.10.1007/978-3-030-01234-2_49 ↗
56. M. Tan, Q.V. Le, EfficientNetV2: Smaller Models and Faster Training, (2021). 〈http://arxiv.org/abs/2104.00298〉.
57. J. Hu, L. Shen, S. Albanie, G. Sun, E. Wu, Squeeze-and-Excitation Networks, (2019). 〈http://arxiv.org/abs/1709.01507〉.
58. A novel approach of online monitoring for laser powder bed fusion defects: air-borne acoustic emission and deep transfer learning10.1016/j.jmapro.2023.07.064 ↗
59. Surface morphology inspection for directed energy deposition using small dataset with transfer learning10.1016/j.jmapro.2023.03.016 ↗
60. Q.-L.Z.Y.-B. Yang, SA-Net: Shuffle Attention for Deep Convolutional Neural Networks, (2021). 〈http://arxiv.org/abs/2102.00240〉.
61. Y. Wu, K. He, Group Normalization, (2018). 〈http://arxiv.org/abs/1803.08494〉.
62. Wu, K., Otoo, E., & Suzuki, K. (2008). Two Strategies to Speed up Connected Component Labeling Algorithms. Lawrence Berkeley National Laboratory. Retrieved from 〈https://escholarship.org/uc/item/5pc9s496〉.
63. Topological structural analysis of digitized binary images by border following10.1016/0734-189X(85)90016-7 ↗
64. Dean · 2017
65. X-ray computed tomography for additive manufacturing: a review10.1088/0957-0233/27/7/072001 ↗
66. Methods for rapid pore classification in metal additive manufacturing10.1007/s11837-019-03761-9 ↗
67. Investigation into the effect of process parameters on microstructural and physical properties of 316L stainless steel parts by selective laser melting10.1007/s00170-014-6297-2 ↗
68. Data processing techniques for in-situ monitoring in L-PBF process10.1016/j.jmapro.2022.06.062 ↗
69. Role of porosity defects in metal 3D printing: formation mechanisms, impacts on properties and mitigation strategies10.1016/j.mattod.2022.08.014 ↗
70. A scalable predictive model and validation for residual stress and distortion in selective laser melting10.1016/j.cirp.2018.04.105 ↗
71. On the delamination and crack formation in a thin wall fabricated using laser solid freeform fabrication process: an experimental–numerical investigation10.1016/j.optlaseng.2009.06.010 ↗
72. Residual stresses in selective laser sintering and selective laser melting10.1108/13552540610707013 ↗
73. In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks10.1016/j.isatra.2018.07.021 ↗