journal article·2022-05

Mechanical and magnetic properties of TRIP690 steel strengthened by strain-induced martensite

Yuqin Peng, Haihong Huang

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Abstract

The sensitivity of the induced magnetic signals to microstructures makes it possible to characterize nondestructively the mechanical properties of ferromagnetic materials. In this study, utilizing the hysteresis loop measurement and the magnetic Barkhausen noise measurement, the correlation between the mechanical and the magnetic properties of low-alloy multiphase transformation-induced plasticity (TRIP) steel after plastic deformation was investigated, then the strength of the TRIP steel after plastic deformation was characterized. The results showed that, as the strain level increased from 0 to 0.2, the martensite volume fraction of TRIP690 steel increased from 0.78% to 3.64% which contributed to the enhancement of mechanical properties and the change of magnetic properties, and in particular, hardness and tensile strength had linearly increased by approximately 42% and 20%, respectively. Meanwhile, the peak value of the magnetic Barkhausen noise signal (MBNS) increased linearly with the increasing strain levels, the coercivity increased rapidly and then slowly, while the susceptibility decreased exponentially. By theoretically deriving the effects of the ferromagnetic phase volume fraction, dislocation density and residual stress of the TRIP690 steel on susceptibility and coercivity, the variation of the MBNSs under the influence of phase transformation, dislocation and residual stress was analyzed. The above non-destructive magnetic technique makes it possible to evaluate the mechanical response of the TRIP steel after plastic deformation, which allows it to be further applied to the quality evaluation of steel parts in stamping, such as body-in-white for the automotive industry.
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Research context

OpenAlex topics: Magnetic Properties and Applications · Microstructure and Mechanical Properties of Steels · Metallurgy and Material Forming

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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