Experimental investigations on the transformation-induced plasticity in a high tensile steel under varying thermo-mechanical loading

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Abstract

Transformation-induced plasticity (TRIP) also known as transformation plasticity (TP) occurs during solid state phase transformation in the case of applied stress and may lead to irreversible macroscopic distortions in steel components after heat treatment. Particularly, in the context of cost-efficient hot forging, where heat treatment is integrated in the process chain, various complex stress states can occur during the cooling phase due to irregular part geometry, temperature gradients and local differences in the deformation history. Varying local temperature, unsteady stress state or even sudden unloading during the transformation can have a strong impact on the resulting TRIP strain. Thus prediction of the final distortions in hot formed steel components becomes challenging. For this reason process simulation based on the finite element (FE) method offers great opportunities for the accurate virtual process design, reducing time- as well as cost-intensive trial and error cycles. However, a realistic FE-simulation requires reliable mathematical models as well as detailed thermo-mechanical material data. In order to improve the modelling of the material behavior in a hot forging and quenching process, physical simulations for particular process-related time-force-temperature profiles have been carried out on a uniaxial thermo-mechanical testing machine. The relative dilatation of the steel specimens for several applied stresses as well as for the case of sudden unloading have been recorded and evaluated for both compressive and tensile loads. It has been shown that other process parameters (e. g. heating strategy) also have a significant influence on the resulting TRIP strains.

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Behrens, B. A., Bouguecha, A., Bonk, C., & Chugreev, A. (2017). Experimental investigations on the transformation-induced plasticity in a high tensile steel under varying thermo-mechanical loading. Computer Methods in Materials Science, 17(1), 36–43. https://doi.org/10.7494/cmms.2017.1.0573

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