Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling
Abstract
The prediction of fatigue life is critical in the design process, and current models offer a viable alternative to costly and time-consuming experimental fatigue testing. The constitutive fatigue model used integrates low-cycle and high-cycle fatigue behavior. This model is grounded on the concept of fatigue damage evolution and incorporates a moving endurance surface within the stress space, eliminating the need for ambiguous cycle-counting methods. An interesting observation is that many polymers exhibit macroscopic fatigue characteristics, specifically, the form of the S-N curve similar to those observed in metals. Consequently, all fatigue model parameters were expressed in terms of the well-established Coffin–Manson–Basquin model parameters. However, the constitutive mathematical modeling itself is computationally time-consuming, particularly when applied to predict high-cycle fatigue across large design spaces. Therefore, the proposed model was utilized exclusively to generate high-quality data for training machine learning models that offer significantly improved computational efficiency. The high-cycle fatigue design of polymers and other ductile materials, traditionally dependent on expensive and time-consuming experimental methods, is now expedited through an advanced modeling framework that combines constitutive mathematical modeling with AI-based approaches.
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BibTeX
@article{barriere2026fast,
title = {{Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling}},
author = {Thierry Barrière and Stanislas Carbillet and Xavier Gabrion and Christophe Guyeux and Sami Holopainen},
journal = {Polymers},
year = {2026},
month = oct,
url = {https://hal.science/hal-05605032v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling
AU - Thierry Barrière
AU - Stanislas Carbillet
AU - Xavier Gabrion
AU - Christophe Guyeux
AU - Sami Holopainen
PY - 2026
DA - 2026/10/27
JO - Polymers
UR - https://hal.science/hal-05605032v1
AB - The prediction of fatigue life is critical in the design process, and current models offer a viable alternative to costly and time-consuming experimental fatigue testing. The constitutive fatigue model used integrates low-cycle and high-cycle fatigue behavior. This model is grounded on the concept of fatigue damage evolution and incorporates a moving endurance surface within the stress space, eliminating the need for ambiguous cycle-counting methods. An interesting observation is that many polymers exhibit macroscopic fatigue characteristics, specifically, the form of the S-N curve similar to those observed in metals. Consequently, all fatigue model parameters were expressed in terms of the well-established Coffin–Manson–Basquin model parameters. However, the constitutive mathematical modeling itself is computationally time-consuming, particularly when applied to predict high-cycle fatigue across large design spaces. Therefore, the proposed model was utilized exclusively to generate high-quality data for training machine learning models that offer significantly improved computational efficiency. The high-cycle fatigue design of polymers and other ductile materials, traditionally dependent on expensive and time-consuming experimental methods, is now expedited through an advanced modeling framework that combines constitutive mathematical modeling with AI-based approaches.
SN - 2073-4360
ER -