MULTIAXIAL LOW CYCLE FATIGUE LIFE PREDICTION MODEL OF THE METAL MATERIAL CONSIDERING ADDITIONAL STRENGTHENING FFECT
For the equivalent strain model,the additional strengthening effect of the material cannot be considered during multiaxial non-proportional loading,resulting in a defect with a large error in fatigue life prediction,based on the pro-interface theory,the maximum shear strain amplitude was used as the...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2024-10-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.05.023 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | For the equivalent strain model,the additional strengthening effect of the material cannot be considered during multiaxial non-proportional loading,resulting in a defect with a large error in fatigue life prediction,based on the pro-interface theory,the maximum shear strain amplitude was used as the main fatigue damage parameter.At the same time,the phase difference,maximum normal stress and shear stress were composed of an additional damage coefficient as a secondary damage parameter to reflect the additional strengthening effect of the metal material under multiaxial non-proportional loading,and the shear stress on the maximum shear surface in the damage coefficient normalized the maximum normal stress to reflect the influence of the interaction between the two stresses on the fatigue life.The proposed model not only retains all the advantages of the equivalent model parameters without introducing additional empirical fitting constants,but also helps to discover the mechanism of crack generation and reflect the direction of crack elongation.The proposed model and three classical models were verified by using six kinds of metal material data of thin-walled round tubes,after analyzing the data verification results of each model,it is found that the prediction results of the proposed model have higher accuracy and more stable data distribution. |
|---|---|
| ISSN: | 1001-9669 |