预测聚合物混合物中的机械反应与非故意的聚合物碎片,使用基于神经网络的有效构成材料模型
Ninghan Tang1, Pei Hao1, Juan Miguel Tiscar2
1Department of Materials, Textiles and Chemical Engineering (MaTCh), Mechanics of Materials and Structures (MMS), Tech Lane Ghent Science Park-Campus A, Ghent University (UGent), Technologiepark-Zwijnaarde 46, 9052 Ghent, Belgium.
Polymers
|April 12, 2025
概括
这项研究引入了一种新的神经网络方法,用于预测混合循环热塑性塑料的机械性能. 该方法准确地预测了聚合物混合物的行为,有助于优化回收塑料应用.
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
- 计算力学 计算力学 计算力学
背景情况:
- 热塑性塑料的机械回收通常产生不纯的材料,由于混合的聚合物类型.
- 即使少量的污染物聚合物也会对回收热塑性塑料的机械性能产生重大影响.
- 准确预测聚合物混合物的机械行为对于有效利用回收材料至关重要.
研究的目的:
- 开发和评估一种混合方法,将神经网络 (NN) 和基于物理的构成模型结合起来,用于预测聚合物混合物的机械行为.
- 通过从物理模型中推导一个关键的内部变量来最大限度地减少对广泛实验数据的需求.
- 评估模型对各种聚合物混合物的有效性,包括LLDPE/PET,LLDPE/PA6和LDPE/PS.
主要方法:
- 一个神经网络 (NN) 模型与基于物理的构成模型集成.
- 一个关键的内部变量,控制非线性响应,在NN中受过训练.
- 这个内部变量是从物理模型中推导出来的,以减少对实验数据的依赖.
- 混合型号在不同重量分数的LLDPE/PET,LLDPE/PA6和LDPE/PS混合物上进行了测试.
主要成果:
- 基于NN的模型显示,对于所有测试的聚合物混合物,与实验结果有很强的一致性.
- 这种方法有效地预测了不同成分的聚合物混合物的机械行为.
- 模型捕捉不同混合比对机械性能的影响的能力得到了验证.
结论:
- 结合NN和物理模型,提供了一种可靠的方法来预测不纯再生热塑性塑料的机械性能.
- 这种预测能力对于提高回收聚合物材料的质量控制和应用范围至关重要.
- 该研究促进了在工程应用中混合循环聚合物的理解和利用.
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