Using deep learning to identify the depth of metal surface defects with narrowband SAW signals

Ding, Lei; Wan, Haopeng; Lu, Qiangbing; Chen, Zhiheng; Jia, Kangning; Ge, Junyan; Yan, Xuejun; Xu, Xiaodong; Ma, Guanbing; Chen, Xi; Zhang, Haiou; Li, Guokuan; Lu, Minghui; Chen, Yanfeng

doi:10.1016/j.optlastec.2022.108758

Cited by 12 publications

(1 citation statement)

References 39 publications

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“…However rich the information content one can obtain from an interference image extracted from a surface revealing its sensitivity to environmental factors and invisible defects and defect impact to the surface, there is a main drawback in the data visualization creating problems in the unwrapping of defected surfaces and this referred to as fringe pattern complexity [15,16]. This is due to the high resolution of coherent interferometry rendering from one hand possible to investigate real artworks of complicated structural construction as the multi-layered mixed organic-inorganic objects are, including the most common artworks such as panel paintings, icons, wall paintings, frescoes, decorative items on furniture, etc., but from the other very dense complex interferograms due to plethora of defects.…”

Section: Introductionmentioning

confidence: 99%

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

Chen,

Zhou,

et al. 2023

Digital

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In this paper, based on Gaussian 1σ-criterion and histogram segmentation, a weighted least-squares algorithm is applied and validated on digital holographic speckle pattern interferometric data to perform phase separation on the complex interference fields. The direct structural diagnosis tool is used to investigate defects and their impact on a complex antique wall painting of Giotto. The interferometry data is acquired with a portable off-axis interferometer set-up with a phase-shifted reference beam coupled with the object beam in front of the digital photosensitive medium. A digital holographic speckle pattern interferometry (DHSPI) system is used to register digital recordings of interferogram sequences over time. The surface is monitored for as long as it deforms prior to returning to its initial reference equilibrium state prior to excitation. The attempt to separate the whole vs. local defect complex amplitudes from the interferometric data is presented. The main aim is to achieve isolation and visualization of each defect’s impact amplitude in order to obtain detailed documentation of each defect and its structural impact on the surface for structural diagnosis purposes.

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Section: Introductionmentioning

confidence: 99%

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

Chen,

Zhou,

et al. 2023

Digital

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A novel method based on deep learning algorithms for material deformation rate detection

Özdem,

Orak

2024

J Intell Manuf

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Given the significant influence of microstructural characteristics on a material’s mechanical, physical, and chemical properties, this study posits that the deformation rate of structural steel S235-JR can be precisely determined by analyzing changes in its microstructure. Utilizing advanced artificial intelligence techniques, microstructure images of S235-JR were systematically analyzed to establish a correlation with the material’s lifespan. The steel was categorized into five classes and subjected to varying deformation rates through laboratory tensile tests. Post-deformation, the specimens underwent metallographic procedures to obtain microstructure images via an light optical microscope (LOM). A dataset comprising 10000 images was introduced and validated using K-Fold cross-validation. This research utilized deep learning (DL) architectures ResNet50, ResNet101, ResNet152, VGG16, and VGG19 through transfer learning to train and classify images containing deformation information. The effectiveness of these models was meticulously compared using a suite of metrics including Accuracy, F1-score, Recall, and Precision to determine their classification success. The classification accuracy was compared across the test data, with ResNet50 achieving the highest accuracy of 98.45%. This study contributes a five-class dataset of labeled images to the literature, offering a new resource for future research in material science and engineering.

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Structural optimization of photoacoustic transducer with PDMS/CSNPs nanocomposite for fatigue crack detection using laser-induced nonlinear surface waves

Yao,

Li,

et al. 2024

Materials & Design

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Using deep learning to identify the depth of metal surface defects with narrowband SAW signals

Cited by 12 publications

References 39 publications

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

Defect Isolation from Whole to Local Field Separation in Complex Interferometry Fringe Patterns through Development of Weighted Least-Squares Algorithm

A novel method based on deep learning algorithms for material deformation rate detection

Structural optimization of photoacoustic transducer with PDMS/CSNPs nanocomposite for fatigue crack detection using laser-induced nonlinear surface waves

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