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Data-Driven Tailoring Optimization of Thermoset Polymers Using Ultrasonics and Machine Learning.

Gonzalo Seisdedos¹, Milo G Prisbrey¹, Pavel Vakhlamov¹

¹Materials Physics and Applications (MPA-11), Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

|April 12, 2025

Summary

This study introduces a non-destructive ultrasonic and machine learning approach to optimize thermoset polymer properties. This method efficiently predicts material characteristics based on manufacturing parameters, reducing costs and time.

Keywords:

cure kinetics elastic properties machine learning non-destructive evaluation resins thermosets ultrasonics

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Area of Science:

Materials Science
Polymer Science
Non-destructive Testing

Background:

Thermoset polymers are crucial for high-performance applications due to their durability.
Traditional characterization methods are destructive, costly, and time-consuming.
Optimizing thermoset properties requires efficient methods to understand manufacturing impacts.

Purpose of the Study:

To develop a novel non-destructive, data-driven method for tailoring thermoset properties.
To correlate manufacturing parameters (curing temperature, stoichiometry) with material properties.
To enable efficient and reliable optimization of thermoset manufacturing.

Main Methods:

Utilized ultrasonics to monitor curing kinetics via real-time sound speed measurements.
Employed machine learning (k-nearest neighbors) to build predictive models.
Manufactured and tested thermoset epoxy samples with varied curing conditions.

Main Results:

Successfully predicted curing kinetics and final elastic properties using manufacturing parameters.
Demonstrated the ability to predict manufacturing parameters from material properties.
Established a correlation between curing temperature, stoichiometry, and material performance.

Conclusions:

The developed non-destructive method efficiently optimizes thermoset tailoring and manufacturing.
Ultrasonics and machine learning offer a powerful alternative to traditional characterization techniques.
This approach facilitates precise control over thermoset properties for advanced applications.