Revolutionizing NDT 4.0 with Deep Attention Learning for Anomaly Detection (DAL-AD) in Mg-based L-PBF components
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OpenAlex topics: Advanced X-ray and CT Imaging · Nuclear Physics and Applications · Nuclear Materials and Properties
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References (75)
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1. The potential of SLM technology for processing magnesium alloys in aerospace industry10.1007/s43452-020-00033-1 ↗
2. Magnesium role in additive manufacturing of biomedical implants – Challenges and opportunities
3. Additive manufacturing of Mg alloys for biomedical applications: Current status and challenges
4. Additive manufacturing of structural materials10.1016/j.mser.2020.100596 ↗
5. X-ray computed tomography for additive manufacturing: A review10.1088/0957-0233/27/7/072001 ↗
6. Microstructure, mechanical properties, corrosion resistance and cytocompatibility of WE43 Mg alloy scaffolds fabricated by laser powder bed fusion for biomedical applications10.1016/j.msec.2020.111623 ↗
7. Effect of laser remelting on printability, microstructure and mechanical performance of Al-Mg-Sc-Zr alloy produced by laser powder bed fusion10.1016/j.jallcom.2023.171287 ↗
8. Additive manufacturing of magnesium alloys
9. 3D printing of Aluminium alloys: Additive manufacturing of Aluminium alloys using selective laser melting10.1016/j.pmatsci.2019.100578 ↗
10. Microstructure evolution and mechanical properties of a high-strength Mg-10Gd-3Y–1Zn-0.4Zr alloy fabricated by laser powder bed fusion
11. Additive manufacturing of high-strength crack-free Ni-based Hastelloy X superalloy
12. Microstructure evolution and mechanical properties of in-situ Ti6Al4V–TiB composites manufactured by selective laser melting10.1016/j.compositesb.2020.108567 ↗
13. Simultaneously enhanced strength and ductility of Cu-15Ni-8Sn alloy with periodic heterogeneous microstructures fabricated by laser powder bed fusion
14. Hot cracking in ZK60 magnesium alloy produced by laser powder bed fusion process10.1016/j.matlet.2021.130283 ↗
15. A synergic approach of deep learning towards digital additive manufacturing: A review10.3390/a15120466 ↗
16. A synergic approach of deep learning towards digital additive manufacturing: A review10.3390/a15120466 ↗
17. Nondestructive testing (NDT)
18. NDE 4.0: Progress, promise, and its role to industry 4.010.1016/j.ndteint.2023.102957 ↗
19. Flash thermography to detect and evaluate impacts in polycarbonate parts produced by additive manufacturing10.1016/j.ndteint.2024.103163 ↗
20. Evidence for contributions of lack-of-fusion defects and dislocations to acoustic nonlinearity and loss in additively manufactured aluminum10.1016/j.ndteint.2024.103068 ↗
21. High performance simulation of ultrasound inspection of porosity in additive manufactured parts10.1016/j.ndteint.2023.102931 ↗
22. Quality assessment of SUS316L fabricated by metal additive manufacturing with eddy current inspection10.1016/j.ndteint.2023.102901 ↗
23. Simonyan · 2014
24. U-Net: Convolutional networks for biomedical image segmentation
25. Oktay · 2018
26. TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers10.1016/j.media.2024.103280 ↗
27. Swin-Unet: Unet-like pure transformer for medical image segmentation
28. In-situ monitoring of sub-surface and internal defects in additive manufacturing: A review10.1016/j.matdes.2022.111063 ↗
29. Application of automation for in-line quality inspection, a zero-defect manufacturing approach10.1016/j.jmsy.2022.12.010 ↗
30. Ex-situ porosity classification in metallic components by laser metal deposition: A machine learning-based approach10.1016/j.jmapro.2020.12.048 ↗
31. Automated defect analysis of additively fabricated metallic parts using deep convolutional neural networks
32. 3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts10.1088/1361-6501/ac459a ↗
33. Flaw detection in multi-laser powder bed fusion using in-situ coaxial multi-spectral sensing and deep learning
34. Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning
35. Kim F, Pintar AL, Scott JHJ, Garboczi E. Evaluation of flaw detection algorithm using simulated X-Ray computed tomography of ground truth data. 2023. 2023-10-04 04:10:00.
36. Ansari · 2021
37. Wong V, Ferguson M, Law K, Lee Y-T, Witherell P. Segmentation of additive manufacturing defects using U-Net. 2022. 2022-06-30 04:06:00.
38. The application of convolutional neural networks (CNNs) to recognize defects in 3D-printed parts10.3390/ma14102575 ↗
39. Automated layerwise detection of geometrical distortions in laser powder bed fusion
40. Deep learning based porosity segmentation in X-ray CT measurements of polymer additive manufacturing parts10.1016/j.procir.2021.01.157 ↗
41. X-ray CT and image analysis methodology for local roughness characterization in cooling channels made by metal additive manufacturing
42. XCT analysis of the influence of melt strategies on defect population in Ti–6Al–4V components manufactured by selective electron beam melting10.1016/j.matchar.2015.02.008 ↗
43. Laboratory X-ray tomography for metal additive manufacturing: Round robin test
44. Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights10.1016/j.matdes.2019.108385 ↗
45. Taghanaki · 2019
46. A new approach for the morphological segmentation of high-resolution satellite imagery10.1109/36.905239 ↗
47. Ferguson · 2018
48. A coarse-grained regularization method of convolutional kernel for molten pool defect identification10.1115/1.4045294 ↗
49. A survey on U-shaped networks in medical image segmentations10.1016/j.neucom.2020.05.070 ↗
50. U-Net and its variants for medical image segmentation: A review of theory and applications10.1109/ACCESS.2021.3086020 ↗
51. EMED-UNet: An efficient multi-encoder-decoder based UNet for medical image segmentation10.1109/ACCESS.2023.3309158 ↗
52. CT image segmentation of bone for medical additive manufacturing using a convolutional neural network10.1016/j.compbiomed.2018.10.012 ↗
53. Clinically-inspired automatic classification of ovarian carcinoma subtypes
54. On the current research progress of metallic materials fabricated by laser powder bed fusion process: a review10.1016/j.jmrt.2022.07.085 ↗
55. Effect of laser speed, layer thickness, and part position on the mechanical properties of maraging 300 parts manufactured by selective laser melting10.1016/j.msea.2019.138425 ↗
56. Survey of 3D image segmentation methods
57. The effectiveness of different thresholding techniques in segmenting micro CT images of porous carbonates to estimate porosity10.1016/j.petrol.2018.12.063 ↗
58. Oktay · 2018
59. A review on the attention mechanism of deep learning10.1016/j.neucom.2021.03.091 ↗
60. Anatomically Constrained Neural Networks (ACNNs): Application to cardiac image enhancement and segmentation10.1109/TMI.2017.2743464 ↗
61. Hassanin · 2022
62. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: International conference on artificial intelligence and statistics. 2010.
63. Approach to weld segmentation and defect classification in radiographic images of pipe welds10.1016/j.ndteint.2021.102597 ↗
64. Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays10.1016/j.ndteint.2020.102345 ↗
65. Automated weld defect segmentation from phased array ultrasonic data based on U-net architecture10.1016/j.ndteint.2024.103165 ↗
66. Transformer-based approach for printing quality recognition in fused filament fabrication10.1038/s44334-025-00025-0 ↗
67. Safdar · Expert Syst Appl · 2024 · 25110.1016/j.eswa.2024.123974 ↗
68. Defect transformer: An efficient hybrid transformer architecture for surface defect detection10.1016/j.measurement.2023.112614 ↗
69. Defect-aware transformer network for intelligent visual surface defect detection10.1016/j.aei.2023.101882 ↗
70. Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing10.1016/j.jmsy.2022.06.010 ↗
71. Wong · 2021
72. An image segmentation framework for in-situ monitoring in laser powder bed fusion additive manufacturing10.1016/j.ifacol.2022.11.280 ↗
73. Contrastive learning-based semantic segmentation for in-situ stratified defect detection in additive manufacturing10.1016/j.jmsy.2023.05.001 ↗
74. Transformer neural network based real-time process monitoring and direct visualization of top-down vat photopolymerization
75. Hatamizadeh · 2021