Related Experiment Video
Updated: Jun 22, 2025
09:06
Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing
Published on: July 3, 2020
7.2K
Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning
Paripat Kraisornkachit1,2, Masanobu Naito1,2, Chao Kang3
1Data-Driven Polymer Design Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Ibaraki 305-0047, Japan.
Materials (Basel, Switzerland)
|June 27, 2024
Summary
This study optimized epoxy resin properties, achieving a 50% reduction in prediction deviation using machine learning and active learning. Researchers identified conditions surpassing the trade-off boundary for adhesive joint strength and elastic modulus.
Area of Science:
- Materials Science
- Polymer Science
- Mechanical Engineering
Background:
- Concurrent study of material properties is vital but resource-intensive.
- Conflicting property requirements for different applications create trade-offs.
Purpose of the Study:
- Investigate adhesive joint strength and elastic modulus in epoxy resins.
- Develop a machine learning model to predict material properties.
- Identify conditions to overcome property trade-offs.
Main Methods:
- Non-destructive indentation for elastic modulus determination.
- Active learning and Bayesian optimization for model refinement.
- Fabrication of smooth, homogeneous polymeric specimens.
Main Results:
- A machine learning model trained on 32 conditions predicted 256 outcomes.
- Active learning reduced prediction deviation by 50% while maintaining accuracy.
- Identified conditions yielding 25.2 MPa adhesive strength and 182.5 MPa elastic modulus.
Conclusions:
- Machine learning and active learning effectively predict and optimize material properties.
- Bayesian optimization identified conditions surpassing the adhesive strength-elastic modulus trade-off boundary.
- The developed methods offer a pathway to enhanced material performance.
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