Evaluation of the applicability of different critical distance models in component high cycle fatigue research: Both experimental verification and parameter error influence analysis

For critical engine parts, such as crankshafts, fatigue strength is one of the most important parameters in the design and manufacturing stage. In previous work, fatigue limit loads of the crankshafts with different structural features were predicted based on different theories of critical distance (TCDs). The results showed that the definition of the TCD had an obvious impact on the accuracy of such predictions. This paper, first the critical distance was determined based on the limit stress distribution of a given crankshaft and the definition of the approach. Then, the fatigue limit load of another crankshaft was predicted based on the parameters obtained in the previous step. Finally, corresponding experimental verification and model parameter error influence analysis were conducted to evaluate the accuracies of the predictions. The results showed that for the modified indirect-defined TCD (ITCD), the predictions based on the line and point methods were approximately equal, and the parameter errors had an obvious impact on the predictions. However, for the direct-defined TCD (DTCD), the critical line approach had much better accuracy than the critical point approach, and the DTCD was much less sensitive to the model parameter errors than the ITCD approach, therefore, the DTCD approach is much more suitable for actual engineering applications.

Automated summary

Fatigue strength is a crucial parameter for critical engine components like crankshafts during the design and manufacturing process. The accuracy of predicting fatigue limit loads for crankshafts with different features is influenced by the definition of critical distance theories. Experimental verification and model parameter error analysis showed that the direct-defined TCD approach is more suitable for engineering applications due to its better accuracy and lower sensitivity to parameter errors.