Machine-Learning-Assisted Viscoelastic Characterization of PC/ABS Blends via Multi-Frequency Dynamic Mechanical Analysis | JoVE Visualize

Area of Science:

Materials Science
Polymer Physics
Computational Materials Science

Background:

Characterizing viscoelastic properties of polymer blends like PC/ABS is crucial for material design.
Traditional methods like DMA can be time-consuming and require expert interpretation.
Machine learning offers potential for accelerated and accurate prediction of material behavior.

Purpose of the Study:

To combine multi-frequency dynamic mechanical analysis (DMA) with machine learning (ML) for characterizing and predicting PC/ABS blend viscoelastic properties.
To evaluate and compare the performance of various data-driven ML models against a physics-informed NeuralWLF model.
To establish a quantitative criterion for validation stringency in DMA-ML model evaluation.

Main Methods:

Multi-frequency DMA temperature sweeps were performed on a PC/ABS blend.
Data-driven models (RF, XGB, SVR, MLP) and a physics-informed NeuralWLF model were trained and validated.
A hierarchical validation framework, including temperature-blocked cross-validation and leave-one-feature-out (LOFO), was employed.
A systematic block size sweep was conducted to investigate validation inflation and establish a gap-to-FWHM ratio criterion.

Main Results:

DMA yielded a glass transition range of 115.8-123.2 °C and frequency sensitivity of 7.18 °C/decade.
The physics-informed NeuralWLF model demonstrated superior cross-frequency generalization (R2>0.92) with interpretable Williams-Landel-Ferry (WLF) parameters.
A physics-data crossover was identified at a gap/FWHM ratio of approximately 2, beyond which NeuralWLF outperformed data-driven models.
Curriculum learning improved NeuralWLF performance under stringent validation conditions (30 °C validation, R2=0.731).

Conclusions:

Honest evaluation of DMA-ML models necessitates validation gaps exceeding characteristic feature widths.
The proposed gap/FWHM ratio serves as a quantitative criterion for assessing validation stringency.
Physics-informed models like NeuralWLF offer advantages in generalization and interpretability for DMA data, especially beyond the identified physics-data crossover point.