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Rubber aging life prediction based on interpolation and improved time-temperature superposition principle
With focus on quickly and accurately predicting and evaluating the aging performance degradation of rubber at room temperature, the pseudo-failure life at each different acceleration temperature is proposed to be calculated by interpolation method based on indoor high temperature accelerated aging d...
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Published in: | Materials research express 2022-01, Vol.9 (1), p.15301 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | With focus on quickly and accurately predicting and evaluating the aging performance degradation of rubber at room temperature, the pseudo-failure life at each different acceleration temperature is proposed to be calculated by interpolation method based on indoor high temperature accelerated aging data, and on the basis of the obtained pseudo-failure life. By introducing the time-temperature equivalence principle, a shift factor obeying to an Arrhenius law is derived, and master curves are built as well for the compression set as for the ultimate mechanical properties. The concept of the sum of squares of dispersion coefficient errors is proposed to evaluate the prediction accuracy. Meanwhile a quantitative calculation method that considers the effect of temperature on the performance degradation curve and the shift factor is innovatively proposes. The results show that the proposed optimization method based on the traditional time-temperature superposition principle can quickly process the aging life at room temperature, and the prediction results are distributed within the 3-fold dispersion line, which can well meet the engineering requirements. The reduction of the DSC value from 1.4164 to 1.0828 further demonstrates the effectiveness of the proposed method above. This method can provide some reference for other related polymer materials accelerated aging data processing and life prediction. |
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ISSN: | 2053-1591 2053-1591 |
DOI: | 10.1088/2053-1591/ac45ba |