Tang Ninghan, Hao Pei, Tiscar Juan Miguel, Gilabert Francisco A
Department 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.
Instituto de Tecnología Cerámica (ITC), Asociación de Investigación de las Industrias Cerámicas (AICE), Universitat Jaume I, Campus Universitario Riu Sec, 12006 Castellón, Spain.
Polymers (Basel). 2025 Apr 1;17(7):963. doi: 10.3390/polym17070963.
Current mechanical recycling procedures often fall short of achieving 100% purity in recycled thermoplastics, which typically consist of mixed polymer types. These other polymers, though typically present in small amounts, can significantly affect the mechanical properties of the recycled material. Addressing this issue, this study introduces a neural network (NN) approach combined with a physically-based constitutive model to accurately predict the mechanical behavior of polymer blends of varying compositions. The NN-based method relies on the training of a crucial internal variable controlling the nonlinear response. This variable is derived from the physical model, which minimizes the dependence on extensive experimental data. We evaluated this approach on polymer blends of LLDPE/PET, LLDPE/PA6, and LDPE/PS at various weight fraction ratios. The results demonstrate that the NN-based model effectively aligns with experimental outcomes, enhancing our ability to predict how different blend ratios influence the mechanical properties of polymer blends. This capability is crucial for optimizing the use of recycled polymers in various applications.
当前的机械回收工艺往往难以使回收的热塑性塑料达到100%的纯度，这些热塑性塑料通常由混合聚合物类型组成。这些其他聚合物虽然通常含量较少，但会显著影响回收材料的机械性能。为了解决这个问题，本研究引入了一种神经网络（NN）方法，并结合基于物理的本构模型，以准确预测不同组成的聚合物共混物的机械行为。基于神经网络的方法依赖于对控制非线性响应的关键内部变量的训练。这个变量来自物理模型，从而最大限度地减少了对大量实验数据的依赖。我们在不同重量分数比的线性低密度聚乙烯/聚对苯二甲酸乙二酯（LLDPE/PET）、线性低密度聚乙烯/聚酰胺6（LLDPE/PA6）和低密度聚乙烯/聚苯乙烯（LDPE/PS）聚合物共混物上评估了这种方法。结果表明，基于神经网络的模型与实验结果有效吻合，增强了我们预测不同共混比如何影响聚合物共混物机械性能的能力。这种能力对于优化回收聚合物在各种应用中的使用至关重要。
