Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique; Journal of Thermal Analysis and Calorimetry; Vol. 150, iss. 11
| Parent link: | Journal of Thermal Analysis and Calorimetry.— .— New York: Springer Nature Vol. 150, iss. 11.— 2025.— P. 8189-8205 |
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| সংস্থা লেখক: | |
| অন্যান্য লেখক: | , , , , , , |
| সংক্ষিপ্ত: | Title screen This paper introduces a highly efficient technique, namely, dynamic infrared scanning thermography (DIST), for detecting defects in large-sized carbon fiber-reinforced polymer/aluminum (CFRP/Al) honeycomb composites. The corresponding test specimen with a high aspect ratio was fabricated for experimental validation by using a DIST system. The pseudo-static matrix reconstruction (PSMR) method and static image sequence processing algorithms were, respectively, employed to pre-process and post-process the experimental data. The results indicate that the DIST method can continuously and effectively detect defects in large-sized CFRP/Al specimens. The respective infrared image dataset was produced, and different convolutional neural network (CNN) models and optimizers were combined for training and comparatively performing automatic defect classification. The obtained results indicate that the combination of the SqueezeNet approach and stochastic gradient descent with momentum (SGDM) is the best when considering the training time as a figure of merit. Such combination provided the accuracy of 99.86% with the time cost of 8.6 min. Neglecting time costs, the combination of DarkNet19 and SGDM has proven to be the best ensuring the accuracy of 99.97%. Текстовый файл |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
2025
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.1007/s10973-024-13365-4 |
| বিন্যাস: | বৈদ্যুতিক গ্রন্থের অধ্যায় |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675800 |
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| 200 | 1 | |a Dynamic infrared scanning thermography based on CNN: a novel large-scale honeycomb defect detection and classification technique |f Rui Li, Chiwu Bu, Hongpeng Zhang [et al.] | |
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| 300 | |a Title screen | ||
| 320 | |a References: 60 tit. | ||
| 330 | |a This paper introduces a highly efficient technique, namely, dynamic infrared scanning thermography (DIST), for detecting defects in large-sized carbon fiber-reinforced polymer/aluminum (CFRP/Al) honeycomb composites. The corresponding test specimen with a high aspect ratio was fabricated for experimental validation by using a DIST system. The pseudo-static matrix reconstruction (PSMR) method and static image sequence processing algorithms were, respectively, employed to pre-process and post-process the experimental data. The results indicate that the DIST method can continuously and effectively detect defects in large-sized CFRP/Al specimens. The respective infrared image dataset was produced, and different convolutional neural network (CNN) models and optimizers were combined for training and comparatively performing automatic defect classification. The obtained results indicate that the combination of the SqueezeNet approach and stochastic gradient descent with momentum (SGDM) is the best when considering the training time as a figure of merit. Such combination provided the accuracy of 99.86% with the time cost of 8.6 min. Neglecting time costs, the combination of DarkNet19 and SGDM has proven to be the best ensuring the accuracy of 99.97%. | ||
| 336 | |a Текстовый файл | ||
| 461 | 1 | |t Journal of Thermal Analysis and Calorimetry |c New York |n Springer Nature | |
| 463 | 1 | |t Vol. 150, iss. 11 |v P. 8189-8205 |d 2025 | |
| 610 | 1 | |a Large-sized CFRP/Al honeycomb composites | |
| 610 | 1 | |a Dynamic infrared thermal wave scanning NDT | |
| 610 | 1 | |a Pseudo-static matrix reconstruction | |
| 610 | 1 | |a CNN | |
| 610 | 1 | |a Defect automatic classifcation | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 701 | 0 | |a Rui Li | |
| 701 | 0 | |a Chiwu Bu | |
| 701 | 0 | |a Hongpeng Zhang | |
| 701 | 0 | |a Fei Wang | |
| 701 | 1 | |a Vesala |b G. T. |g Gopi Tilak | |
| 701 | 1 | |a Ghali |3 V. S. |g Venkata Subbarao | |
| 701 | 1 | |a Vavilov |b V. P. |c Specialist in the field of dosimetry and methodology of nondestructive testing (NDT) |c Doctor of technical sciences (DSc), Professor of Tomsk Polytechnic University (TPU) |f 1949- |g Vladimir Platonovich |9 16163 | |
| 712 | 0 | 2 | |a Томский политехнический университет |c 1991- |9 26305 |4 570 |
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| 856 | 4 | |u https://doi.org/10.1007/s10973-024-13365-4 |z https://doi.org/10.1007/s10973-024-13365-4 | |
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