Machine Learning-Driven Paradigm for Polymer Aging Lifetime Prediction: Integrating Multi-Mechanism Coupling and...

Area of Science:

• Materials Science
• Computational Science
• Polymer Science

Background:

• Conventional methods like Arrhenius models and time-temperature superposition have limitations in predicting polymer aging.
• Accurate prediction of polymer aging lifetime is crucial for engineering safety and material design.

Purpose of the Study:

• To systematically review machine learning's role in predicting polymer aging lifetime.
• To establish a framework integrating multi-mechanism coupling with dynamic data-driven modeling for enhanced prediction accuracy.

Main Methods:

• Utilizing support vector machines for nonlinear interactions in multi-stress environments.
• Employing neural networks for cross-scale modeling from molecular dynamics to macroscopic failure.
• Leveraging decision tree models for interpretable feature importance quantification.
• Integrating hybrid machine learning approaches for synergistic benefits.

Main Results:

• Machine learning models demonstrate significant success in predicting polymer aging across various applications.
• These models effectively bridge molecular-level dynamics with macroscopic service-life performance.
• The methodologies provide a data-driven paradigm for mechanism-based simulation of degradation.

Conclusions:

• Machine learning transforms polymer life prediction from empirical extrapolation to mechanism-based simulation.
• This approach offers robust methodological support for engineering safety design in diverse polymer applications.
• The models can handle complex environmental interactions and adapt to real-time monitoring data.