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<p>Surface stress concentration factor (SSCF) is the key index for assessing the fatigue performance of aero-engine blades, which is closely related to surface microtopography. However, it is difficult to theoretically simulate and predict the SSCF due to the complex characteristics of the micro-morphology of the grinding processed surface. In this study, a three-dimensional roughness calculation model was established based on the probabilistic and structural characteristics of the profile grinding process on the surface of the TNM alloy blades, and based on which a theoretical relationship between the process parameters and the SSCF was established, the prediction algorithm for SSCF of the profile grinding driven by process parameters was developed. The verification of the accuracy of the prediction algorithm was completed by 3D reconstruction of the machined surface microtopography and the finite element analysis (FEA) of uniaxial stress stretching. The results show that the prediction results are consistent with the SSCF of the machined surface, with a relative error range of 0.412–4.78%, which realizes the accurate prediction of the SSCF for the profile grinding of TNM alloys and the process control of “Low-stress concentrated grinding.”</p>

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Development of a prediction algorithm for stress concentration factor of surface microtopography under profile grinding

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Zhaoqing Zhang,
  • Kaining Shi,
  • Yaoyao Shi,
  • Huhu Li,
  • Wenbo Huai

摘要

Surface stress concentration factor (SSCF) is the key index for assessing the fatigue performance of aero-engine blades, which is closely related to surface microtopography. However, it is difficult to theoretically simulate and predict the SSCF due to the complex characteristics of the micro-morphology of the grinding processed surface. In this study, a three-dimensional roughness calculation model was established based on the probabilistic and structural characteristics of the profile grinding process on the surface of the TNM alloy blades, and based on which a theoretical relationship between the process parameters and the SSCF was established, the prediction algorithm for SSCF of the profile grinding driven by process parameters was developed. The verification of the accuracy of the prediction algorithm was completed by 3D reconstruction of the machined surface microtopography and the finite element analysis (FEA) of uniaxial stress stretching. The results show that the prediction results are consistent with the SSCF of the machined surface, with a relative error range of 0.412–4.78%, which realizes the accurate prediction of the SSCF for the profile grinding of TNM alloys and the process control of “Low-stress concentrated grinding.”