Microscale-calibrated modeling of the deformation response of dual-phase steels
Source abstract
Abstract
Abstract A combination of micropillar compression tests and microstructure-based numerical simulations were used to determine the flow strength and strain rate partitioning in uniaxial tension in two commercial low-carbon dual-phase sheet steels, DP980 (0.09% C–2.15% Mn–0.60% Si (wt.%)) and DF140T (0.15% C–1.45% Mn–0.30% Si (wt.%)). The two steels have different microstructures, with the martensite volume fraction in DP980 being ∼60%, compared to ∼40% in DF140T. Nevertheless, they exhibit similar uniaxial stress–strain behavior. To determine the microstructural origin of this behavior, micropillar compression specimens from ferrite and martensitic phases in both steels were deformed in uniaxial compression to obtain their individual response. A microstructure-based crystal plasticity model that accounts for non-Schmid behavior in the ferrite phase and contains a detailed description of the hierarchical microstructure of martensite was developed and material parameters were determined by fitting model predictions to the micropillar compression data. The crystal plasticity model was then used to predict the flow stress and strain rate partitioning during uniaxial tensile deformation of the two steels. The ferrite phase in the two steels was found to have similar flow strength. In contrast, the flow stress of martensite in DF140T was found to be approximately twice that in DP980. This strength difference is offset by the difference in martensite volume fraction in the two steels, resulting in nearly identical uniaxial tensile behavior. The strain rate partitioning and interfacial stress distributions in the two steels differ significantly, however, and have important implications on their tensile ductility.
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Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Metallurgy and Material Forming · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
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