New insights into martensite strength and the damage behaviour of dual phase steels
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Abstract
Abstract A detailed investigation of martensite islands in ultra-high strength dual phase (DP) steels using TEM EELS carbon measurements, nano-indentation studies and micro-mechanical modelling has been carried out. EELS analysis showed that the dispersion in the martensite island-to-island carbon content increases at lower intercritical annealing temperatures due to the influence of undissolved cementite. In a coarse-grained DP alloy, the median martensite island nano-hardness values and those calculated from EELS carbon data were in excellent agreement. However, in a fine-grained (microalloyed) DP alloy significant and unexplained softening occurred that is not consistent with the measured martensite carbon content. In both steels, the dispersion in martensite nano-hardness was greater than that expected from the measured carbon variations. Micro-mechanical modelling using the continuous composite approach (CCA) method was employed to calculate the martensite flow stress distribution required to fit the bulk tensile response of the two materials. The median martensite nano-hardness values derived from the fitted CCA stress spectra were in good agreement with those measured by nano-indentation, corroborating the observed martensite softening. These results provide experimental support for the CCA approach and suggest that the physical origins of the martensite stress spectrum can be strongly influenced by mechanisms other than carbon segregation. Finally, these data explain why the beneficial effect of reducing the α'/α phase strength ratio (PSR) on DP damage properties is highly asymmetrical, depending on whether the ferrite is strengthened or the martensite is softened (by tempering).
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Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Hydrogen embrittlement and corrosion behaviors in metals · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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