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Tao Zhou

Profilbild av Tao Zhou




Om mig

Research interests

  • Integrating multiscale experimental and computational tools for metallic materials development
  • Complementary advanced characterization (such as TEM, APT, in situ SAXS/SANS and WAXS) and precipitation kinetics modelling (such as mean-field and full-field methods) of nanoscale precipitation (mostly below 20 nm), such as Cu-rich precipitates, carbides, and intermetallics.
  • Research keywords: alloy development; precipitation kinetics; advanced characterization; physical metallurgy.

Research cases

  • Computational materials design framework for Cu precipitation-strengthened maraging stainless steels.                                                                          Cu, through forming Cu-rich precipitates in bcc-Fe matrix, is an effective alloying element for developing high-performance steels with combination of high-strength and good toughness (the Cu precipitation strengthening allows the reduction of carbon content to obtain same strength). An computational framework including both microstructure modelling and property modelling has been developed based on Cu precipitation-hardening maraging stainless steel 15-5 PH, where experimental tools have been used to setup, calibrate and validate the modelling. To develop models for Cu precipitation kinetics using the Langer-Schwartz-Kampmann-Wagner approach coupled with CALPHAD thermodynamic and kinetic databases, quantitative characterization of Cu precipitates including structure, size, volume fraction, number density and chemical composition were performed for different aging conditions using TEM, APT and in situ SANS. Except for precipitation, other microstructure characteristics like effective grain size and dislocation density were also modelled using semi-empirical models, after being calibrated by experiments, where the dislocation density was quantified using the modified Williamson-Hall and Warren-Averbach method through analyzing the peak broadening of XRD data. Further on, yield strength of the material as a function of aging treatment was modelled using semi-empirical models; the Cu precipitation contribution to the stress-strain curves were modelled using an analytical flow stress model based on strain gradient plasticity theory; The stress-strain curves of the martensite matrix were modelled using a dislocation density-based crystal plasticity model. Through the process, i) an overview of TEM for precipitation analysis in metallic materials were carried out; and ii) an interesting phenomenon of Cu-precipitation mediated formation of reverted austenite was found in the 15-5 PH during aging treatment.

Computational framework for precipitation-strengthened martensitic steels

  • Carbides precipitation engineering for high-performance tool steels.                   Cr, Mo, and V are widely used as alloying elements for carbon steels, especially tool steels and heat resistant steels, to increase hardenability, hardness, thermal stability,etc. They can potentially form carbide phases of V-rich MC, Mo-rich M2C and M6C, Cr-rich M7C3 and M23C6, etc., except for Fe-rich metastable carbides, depending on the chemical composition and process conditions. Firstly, Mulitple carbides precipitation and mechanical properties of a high-performance tool steel were investigated, and it was found that coarse types of carbides were the dominant secondary phases at the tempering conditions, which is not ideal for the optimal performance. Thereafter, some efforts were put to tailor the carbides precipitation using computational thermodynamics and kinetics to achieve the precipitation of high number density of merely fine types of precipitates, as we know from open literature that the fine carbides precipitation has the merits of high precipitation strengthening, weak effects on toughness (also allowing the reduction in carbon content to improve toughness), H trapping for improved resistance to hydrogen embrittlement, higher thermal stability, increasing creep performance,etc.

Computational thermodynamics and kinetics-guided re-engineering of a high-performance tool steel

Profilbild av Tao Zhou