基于实验测试数据的复合材料属性的机器学习驱动预测
Khrystyna Berladir1,2, Katarzyna Antosz3, Vitalii Ivanov2,4
1Department of Applied Materials Science and Technology of Constructional Materials, Faculty of Technical Systems and Energy Efficient Technologies, Sumy State University, 116, Kharkivska St., 40007 Sumy, Ukraine.
Polymers
|March 13, 2025
概括
机器学习准确地预测复合材料的特性,优化填充剂的选择,以提高耐磨性和机械强度. 这种数据驱动的方法减少了实验浪费和成本,促进了可持续的材料设计.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料科学科学 计算材料科学
- 聚合物复合材料 聚合物复合材料
背景情况:
- 对于高性能,成本效益高的复合材料的需求日益增加.
- 需要先进的计算方法来优化复合材料的组成和特性.
- 具有多种填充物的热塑性复合材料对于各种应用至关重要.
研究的目的:
- 应用机器学习 (ML) 来预测和优化热塑性复合材料的功能性质.
- 研究各种纤维,分散和纳米分散填充剂对复合材料性能的影响.
- 在复合材料设计中建立数据驱动的框架,以合理选择填料.
主要方法:
- 材料合成通过粉末金.
- 微结构分析,机械测试和部落学测试.
- 开发和验证ML回归模型用于属性预测 (R平方到0.80).
主要成果:
- 最佳的填料选择显著提高耐磨性 (例如,碳纤维17-25倍,高45-57倍),同时管理机械强度.
- 特定的填充剂,如玄武纤维,高,焦炭,石墨,化,二氧化和PTFE,显示出明显的性能增强.
- 机器学习模型有效地预测了复合材料的特性,解释了高达80%的变化,减少了广泛的实验需求.
结论:
- 机器学习为优化复合材料提供了一个高效,具有成本效益的框架.
- 该研究表明了ML在指导填充剂选择以满足定制复合材料性能方面的潜力.
- 这种方法有助于可持续的工业实践,并符合可持续发展目标9.
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