Fast Fatigue Life Prediction of Polymers Through Combined Constitutive Mathematical and AI-Based Modeling

TL;DR

This paper introduces a new method to predict polymer fatigue life using a combination of mathematical models and AI, reducing the need for expensive experiments.

Contribution

The novel approach combines constitutive modeling with AI to efficiently predict high-cycle polymer fatigue.

Findings

Polymers show S−N curve characteristics similar to metals, enabling parameterization via the Coffin–Manson–Basquin model.

Constitutive models generate high-quality data for training AI models, improving computational efficiency.

The combined framework accelerates high-cycle fatigue design for polymers and ductile materials.

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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