1. Ultrasonic backscattering measurement of hardness gradient distribution in polycrystalline materials.
- Author
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Li, Changze, Chen, Ping, Fu, Tong, and Yu, Xin
- Subjects
- *
PROBABILITY density function , *HARDNESS testing , *ULTRASONIC measurement , *CRYSTAL grain boundaries , *BACKSCATTERING - Abstract
• Establishing the probability density function of grain distribution in hardness gradient distribution polycrystals. • Utilizing synthetic polycrystals to study the influence of grain distribution variation. • Extending the T-T scattering model to incorporate hardness gradient distribution polycrystals. • The minimum mean testing error for hardness distribution of differently induction-hardened 40Cr is 2.55 %. • NDT of hardness distribution in thermally hardened polycrystalline materials is achievable. It is crucial to obtain the internal hardness distribution in polycrystalline materials to evaluate the mechanical performance of components and monitor their service life. Current methods, however, fail to meet the non-destructive evaluation needs for materials with hardness gradient distributions. This paper, based on the principle of grain boundary scattering of ultrasound in polycrystalline materials, combined with the Transverse-to-Transverse Singly-Scattered Response (T-T SSR) theory, proposes an ultrasonic SSR model adapted to hardness gradient distributions. The model elucidates the influence of hardness gradient variations and grain dispersion on ultrasonic scattering. Using DREAM.3D, seven different-scale polycrystalline volumes were constructed to assess the relevance of volume-weighted average grain size and spatial correlation of hardness gradient materials. Finally, induction quenching was applied to 40Cr to induce a gradient hardness distribution internally, followed by ultrasonic backscatter experiments. The results indicate that the theoretical model and the spatial variance of measured signals align well over a relatively long time window. For the specimen with minor curvature, the theoretical hardness distribution obtained by the model is accurate, with an average error of 2.55 % compared to destructive testing data. However, the results for the larger curvature reveal limitations in the model. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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