1. Predicting natural aging effects on fatigue life of CFRP–aluminum adhesive joints using machine learning and accelerated aging data.
- Author
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Karimi, Sajjad and Anvari, Ardavan
- Subjects
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ARTIFICIAL neural networks , *MACHINE learning , *REGRESSION analysis , *FATIGUE life , *FAILURE mode & effects analysis , *HYGROTHERMOELASTICITY , *ADHESIVE joints , *ACCELERATED life testing - Abstract
AbstractThis study investigates the behavior and reliability of CFRP-to-aluminum adhesive joints subjected to hygrothermal conditions under both natural and accelerated aging scenarios. Natural aging durations ranged from 1 to 3 years, while accelerated aging was performed over periods of 100–1200 h in 50-hour intervals. Fatigue life was evaluated using a three-point bending test, revealing a significant degradation in joint performance due to hygrothermal exposure. Key findings include a 25.98% reduction in fatigue life for samples naturally aged for three years and a comparable 27.33% reduction in samples subjected to 1000 h of accelerated aging. Hygrothermal conditions caused notable matrix degradation, transitioning failure modes from cohesive to mixed types (cohesive, adhesive, and fiber tear failures), with a direct impact on joint durability. Machine learning models, including artificial neural network (ANN), support vector regression (SVR), linear regression, polynomial regression, and random forest regression, were employed to predict natural aging durations based on accelerated aging data. Among these, the random forest regressor exhibited the highest predictive accuracy, effectively correlating accelerated aging durations to natural aging conditions. This study provides critical insights into the failure mechanisms and long-term performance of adhesive joints, offering a novel predictive approach to estimate natural aging effects from accelerated tests. These findings highlight the potential for optimizing joint designs to enhance durability and reduce failure risks in operational environments. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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