多目标优化粘合剂联合强度和弹性模块的粘合剂环氧体与主动学习
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
概括
这项研究优化了环氧树脂的性能,通过机器学习和主动学习实现了50%的预测偏差降低. 研究人员确定了超越粘合剂关节强度和弹性模量权衡边界的条件.
科学领域:
- 材料科学 材料科学 材料科学
- 聚合物科学 聚合物科学
- 机械工程 机械工程
背景情况:
- 对材料属性的同时研究至关重要,但资源密集.
- 不同应用程序的相冲突的属性要求会产生权衡.
研究的目的:
- 在环氧树脂中研究粘合性关节强度和弹性模量.
- 开发一个机器学习模型来预测材料特性.
- 确定克服房地产权衡的条件.
主要方法:
- 用于弹性模量测定的非破坏性内.
- 积极学习和贝叶斯优化用于模型改进.
- 制造光滑,均的聚合物样本.
主要成果:
- 在32个条件下训练的机器学习模型预测了256个结果.
- 积极学习减少了50%的预测偏差,同时保持了准确性.
- 确定条件产生25.2 MPa的粘强度和182.5 MPa的弹性模量.
结论:
- 机器学习和主动学习有效地预测和优化材料属性.
- 贝叶斯优化确定了超越粘合强度-弹性模量权衡边界的条件.
- 开发的方法为提高材料性能提供了一条途径.
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