Prediction of case depth for deep carburising heat treatments based on the spatial gradient feature of the grain size profile

ABSTRACT

Precise evaluation of carburising heat treatment is necessary to avoid failure and degradation of high-performance industrial mechanical components. In this work, a novel non-destructive detection method is proposed for the deep carburised case depth (>2 mm) combining spatial ultrasonic backscatter technique and linear regression model. To create various carburised case depths, three groups of cylindrical 20CrMnMo steel samples were heat-treated in various cycles (Carburising, diffusing, quenching at 850–870°C (holding in range of 2.5–3 h) and tempering). The carburising heat treatment results in a gradient microstructure within the surface region caused by changes in grain size. We employed a transverse-to-transverse scattering model to provide the gradient feature of the grain size profile in the depth direction. The extracted gradient profiles of different carburised case depths exhibit two overlapped growth rates with high- and low-slope factors, respectively. With the increase of the carburised case depth, the positions of the centre points increase and the slope factors decrease. Based on these observations, a modified double sigmoidal function was utilised to parametrise the gradient profile curve. The relationship among the selected gradient feature parameters and various carburised case depths has been established. The predicted results were compared to hardness measurements with good agreement.

KEYWORDS:

• Carburizing heat treatment
• ultrasonic backscatter
• grain size
• gradient microstructure
• double sigmoidal function

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

T.F. conceived this article, performed the investigation, and wrote the original draft; P.C. data curation, and validation; and A.Y. revised the article and provided some valuable suggestions. All authors have read and agreed to the published version of the manuscript.

Additional information

Funding

This research was funded by National Natural Science Foundation of China (grant number 51374264), and Science and Technology Major Project of Chongqing (grant number cstc2018jszx-cyztzxX0032).