深度学习用于预测天然纤维聚合物复合材料的性能
Ivan P Malashin1, Dmitry Martysyuk2, Vladimir Nelyub2,3
1Bauman Moscow State Technical University, 105005, Moscow, Russia. ivan.p.malashin@gmail.com.
Scientific reports
|July 30, 2025
概括
深度学习模型通过分析纤维-矩阵相互作用和表面处理,准确地预测聚合物复合材料的机械性能. 这些先进的模型比传统的材料表征方法有了显著的改进.
科学领域:
- 材料科学与工程 材料科学与工程
- 聚合物科学 聚合物科学
- 计算材料科学科学 计算材料科学
背景情况:
- 深度学习 (DL) 越来越多地用于预测聚合物特性,这是由于数据的多样性.
- 像卷积神经网络-多层感知器 (CNN-MLP) 和深度神经网络 (DNNs) 这样的专业DL架构在预测机械,热和化学性质方面表现出高准确性.
- DNN擅长在异质数据集中捕获复杂的非线性关系,这对于材料的表征和优化至关重要.
研究的目的:
- 研究天然纤维 (亚麻,棉花,西萨尔,大麻) 和表面处理对聚合物基质 (PLA,PP,环氧) 机械性能的影响.
- 开发和比较各种回归模型,包括DNN,用于预测这些复合材料的机械行为.
- 确定最佳的DNN架构,以最大限度地提高预测准确度并最大限度地减少错误.
主要方法:
- 四种天然纤维以30%的重量被纳入三种聚合物矩阵中,表面处理为未经处理,性和性.
- 用挤出,注塑或造来准备样品,然后进行机械性质测试 (拉伸强度,模量,延伸,冲击性) 和密度测定.
- 回归模型 (线性,随机森林,梯度增强,DNN) 在180个实验样本上进行训练,使用引导将其增加到1500个. 使用Optuna.com进行了超参数优化.
主要成果:
- 最好的DNN模型,包括四个隐藏层,ReLU激活,20%掉机和AdamW优化器,实现了高达0.89.89的R平方值.
- 与梯度增强相比,这种DNN模型显示了9-12%的平均绝对误差 (MAE) 减少.
- DNN的卓越性能归因于它能够捕捉光纤-矩阵相互作用,表面处理和处理参数之间的非线性协同作用.
结论:
- 深度神经网络对于预测天然纤维增强聚合物复合材料的机械行为非常有效.
- 该研究强调了考虑材料组件,表面变化和加工条件之间的复杂相互作用对于准确的属性预测的重要性.
- 开发的DNN模型为聚合物复合材料领域的材料表征和优化提供了强大的工具.
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