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It is crucial to model the temperature distribution and assess its impact on residual stress and distortion to ensure the quality of quenched parts. However, quenching is a complex, multi-scale, and multi-physics problem involving many interplay phenomena, such as rapid evaporation, condensation, and thermal-mechanical interactions. The physical complexity makes developing an accurate and efficient quenching model to achieve this objective a significant challenge. This paper presents a coupled data-physics thermo-mechanical simulator (DPTMS) for quenching processes. DPTMS is built on a data-physics coupling framework, which leverages physics-informed machine learning and finite element method to achieve accurate temperature prediction and thermo-mechanical analysis. Firstly, a physics-informed machine learning model is developed to quickly reconstruct the full-field temperature profile from limited temperature monitoring data. In the machine learning temperature model, we apply a re-combination method to reorganize monitored temperature data to remedy the challenge of data scarcity. Following this, we develop a machine learning framework using appropriate neural network architectures and inherent physics laws to restructure the 3D temperature profiles. Subsequently, the machine learning-based temperature model is seamlessly integrated into a parallelized finite element-based thermo-mechanical model using FEniCS to predict residual stress and distortion. The accuracy of this coupled machine learning and FEniCS approach are validated with high-fidelity simulation and experimental data. The comparison with existing models in terms of accuracy and efficiency is presented to show the superior performance of the proposed DPTMS.<\/jats:p>","DOI":"10.1007\/s00366-025-02234-9","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T07:20:23Z","timestamp":1768375223000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DPTMS: data-physics coupling thermo-mechanical simulation method of quenching process"],"prefix":"10.1007","volume":"42","author":[{"given":"Yongjia","family":"Xu","sequence":"first","affiliation":[]},{"given":"Ze","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jinhui","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"2234_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.msea.2020.139684","volume":"794","author":"R Lehnert","year":"2020","unstructured":"Lehnert R, Wagner R, Burkhardt C, Clausnitzer P, Weidner A, Wendler M, Volkova O, Biermann H (2020) Microstructural and mechanical characterization of high-alloy quenching and partitioning trip steel manufactured by electron beam melting. 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