| Title: | A deep learning framework for full-field thermal field distribution prediction from digital image correlation strain measurements |
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| Authors: | ID Grebo, Alen (Author)
ID Novak, Nejc (Author)
ID Panić, Branislav (Author)
ID Petrović, Nikola (Author)
ID Krstulović-Opara, Lovre (Author) |
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| Files: |  applsci-16-00460.pdf (2,67 MB)
MD5: 58F318B1C16EE202CA1882D3A62BA5CE
 https://www.mdpi.com/2076-3417/16/1/460
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Digital Image Correlation (DIC) and infrared thermography (IRT) are widely used for full-field experimental analysis of materials and structures; however, direct thermal measurements are often constrained by limited access, thermally opaque safety enclosures, or the availability of infrared equipment. This study presents a deep learning-based framework for predicting full-field temperature distributions directly from a DIC-derived effective strain field. A supervised U-Net regression model was trained on paired effective strain–temperature data obtained from high-speed three-point bending experiments on aluminum specimens. The network learns a direct mapping between effective strain fields and corresponding temperature fields without requiring explicit thermomechanical modelling. The model’s performance was evaluated on an independent test set using RMSE, MAE, SSIM, and the coefficient of determination. The proposed framework achieved a coefficient of determination of up to R2 = 0.985 and showed strong spatial agreement with measured temperature fields, particularly during highly mechanically active deformation stages. These results demonstrate that reliable full-field temperature distributions can be reconstructed solely from strain measurements, providing a practical alternative to infrared thermography in experimental configurations where thermal imaging is impractical or unavailable. |
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| Keywords: | Digital Image Correlation (DIC), infrared thermography, U-net, deep learning, thermomechanical coupling, full-field temperature prediction, strain–temperature mapping, image to image regression |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 06.12.2025 |
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| Article acceptance date: | 30.12.2025 |
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| Publication date: | 01.01.2026 |
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| Publisher: | MDPI |
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| Year of publishing: | 2026 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 16, iss. 1, [article no.] 460 |
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| PID: | 20.500.12556/DKUM-97009  |
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| UDC: | 004.9 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 266230531 |
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| DOI: | 10.3390/app16010460 |
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| Publication date in DKUM: | 11.02.2026 |
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| Views: | 145 |
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| Downloads: | 12 |
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| Metadata: |    |
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| Categories: | Misc.
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