Thermography Techniques for Examination of Metals

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1695

Special Issue Editors

School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: thermal wave imaging; signal processing; photothermal science and detection

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Guest Editor
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: non-destructive testing and evulation; machine learning; silicon solar cell; 3D printing; in-process monitoring
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Special Issue Information

Dear Colleagues,

Non-destructive testing and evaluation (NDT&E) plays an increasing role in the modern industry. if compared to the traditional NDT&E approaches (such as X-ray, ultrasonic, and eddy current, etc.), thermography have advantages such as fast, safe, inexpensive, and large detection area. Recently thermography techniques expecially the active infrared thermography are extremely attractive to scientists and public. Thermography techniques have been widely used in aerospace, petrochemical, automotive and other fields, becoming a key way to solve some difficult engineering problems. This Special Issue is focused on non-destructive testing and evaluation (NDT&E) via mid-infrared and far-infrared thermography. Both theoretical research and industrial application are welcome, which are within but not limited to the following fields:

  • Multimode thermography techniques.
  • Interaction between physical field and material.  
  • Applications in material evaluation field.
  • Signal analysis and feature extraction algorithms.
  • Advanced industrial applications
  • Image processing.

Dr. Fei Wang
Prof. Dr. Junyan Liu
Guest Editors

Manuscript Submission Information

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Keywords

  • thermography
  • non-destructive testing
  • NDT&E
  • infrared
  • signal analysis
  • image processing

Published Papers (1 paper)

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Research

17 pages, 12236 KiB  
Article
Convolution Neural Network Fusion Lock-In Thermography: A Debonding Defect Intelligent Determination Approach for Aviation Honeycomb Sandwich Composites (HSCs)
by Xinjian Wang, Mingyu Gao, Fei Wang, Feng Yang, Honghao Yue and Junyan Liu
Metals 2023, 13(5), 881; https://doi.org/10.3390/met13050881 - 2 May 2023
Cited by 3 | Viewed by 1321
Abstract
This report is on convolution neural network (CNN) fusion lock-in thermography, which can implement the intelligent identification of defects for aviation honeycomb sandwich composites (HSCs). First, HSCs specimens with subsurface delamination defects were fabricated and stimulated by halogen lamps according to sinusoidal modulation, [...] Read more.
This report is on convolution neural network (CNN) fusion lock-in thermography, which can implement the intelligent identification of defects for aviation honeycomb sandwich composites (HSCs). First, HSCs specimens with subsurface delamination defects were fabricated and stimulated by halogen lamps according to sinusoidal modulation, and the defects were reliably inspected using lock-in thermography. The amplitude and phase images (commonly referred to as feature images) were obtained by using a digital lock-in correlation algorithm. Furthermore, these feature images were changed into gray or color-level image formalism datasets, which is pre-processed in ways including contrast enhancement, threshold segmentation as well as mosaic data augmentation. Finally, the four-layer feature pyramid structure and ransformer are combined and introduced to the popular YOLOv5 CNN model, and a YOLOLT CNN model is formed to realize the defect identification. The average precision (AP) in the defect identification of HSCs in complex environments (contains noise and other objects) reached 93.2% and achieved an average recognition speed of 0.6 s/image. Full article
(This article belongs to the Special Issue Thermography Techniques for Examination of Metals)
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