20.500.12556/DKUM-97009 A deep learning framework for full-field thermal field distribution prediction from digital image correlation strain measurements 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. Digital Image Correlation (DIC) infrared thermography U-net deep learning thermomechanical coupling full-field temperature prediction strain–temperature mapping image to image regression digitalna korelacija slik infrardeča termografija globoko učenje termomehanska sklopka napoved temperature celotnega polja preslikava deformacije in temperature regresija slike do slike true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2076-3417/16/1/460 Članek v reviji 2026-02-11 14:25:37 2026-02-11 14:25:37 2026-02-12 03:14:19 0000-00-00 00:00:00 2026 0 0 19 str. iss. 1, [article no.] 460 Vol. 16 Jan. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-12-06 2025-12-30 2026-01-01 004.9 266230531 10.3390/app16010460 2076-3417 applsci-16-00460.pdf applsci-16-00460.pdf 1 58F318B1C16EE202CA1882D3A62BA5CE 630d9ba73e8eaf7aca4101a64c456eeb87caf20aab7cb374d8a237bad597d321 213e2e22-074f-11f1-b759-00155d00019a https://dk.um.si/Dokument.php?lang=slv&id=203220 https://www.mdpi.com/2076-3417/16/1/460 0 294487a7-074f-11f1-b759-00155d00019a https://dk.um.si/Dokument.php?lang=slv&id=203221 Fakulteta za strojništvo 0 0 0