Fatigue Strength Evaluation for Remanufacturing Impeller of Centrifugal Compressor Based on Modified Grey Relational Model
The fatigue strength, as the essential basis of residual life evaluation, is required to be obtained timely for remanufacturing. Since impeller damage is characterized with very-high-cycle fatigue (VHCF), it is difficult to directly test the strength data. The transformation method of multisource st...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/1236130 |
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| Summary: | The fatigue strength, as the essential basis of residual life evaluation, is required to be obtained timely for remanufacturing. Since impeller damage is characterized with very-high-cycle fatigue (VHCF), it is difficult to directly test the strength data. The transformation method of multisource strength data is proposed to predict fatigue strength for impeller based on grey relational theory. The multisource strength data, as factor space, primarily include available existing experimental data and operating data, while the strength data of the remanufacturing impeller are taken as target data. The fatigue strength model of material and component are presented to analyze the influence factors of remanufacturing target strength. And similar material provides a theoretical basis for selecting reference data reasonably. Considering the correlation and difference between available data and target data, the grey relational function is established, and the correction function of the target residual is brought forward to reduce the transformation deviation. The entropy-weight theory is implemented to determine the different impacts of multisource data on target strength. A test case, predicting the unknown impeller fatigue strength with various impellers, is applied to validate the proposed transformation method, and the results show that the predicted strength data are consistent with the experimental data well. |
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| ISSN: | 1687-8434 1687-8442 |