Tian Defang, Alexenko Vladislav O, Stepanov Dmitry Yu, Buslovich Dmitry G, Zelenkov Alexey A, Panin Sergey V
Department of Materials Science, Engineering School of Advanced Manufacturing Technologies, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.
Laboratory of Mechanics of Polymer Composite Materials, Institute of Strength Physics and Materials Science, Siberian Branch of Russian Academy of Sciences, 634055 Tomsk, Russia.
Polymers (Basel). 2025 May 25;17(11):1468. doi: 10.3390/polym17111468.
In this study, laminates based on polyetherimide (PEI) with contents of carbon fibers (CFs) from 55 to 70 wt.% were fabricated by thermoforming (TF) and ultrasonic additive manufacturing (UAM) methods. The UAM laminates with CF contents above 55 wt.% possessed shear strengths lower by 40% in comparison with those of the TF ones, due to insufficient amounts of the binder in the prepregs to form reliable interlaminar joints. For enhancing the shear strength of the laminates with a CF content of 70 wt.%. up to the levels of the TF ones, extra resin layers with thicknesses of 50, 100, and 150 μm were deposited. By ranking the UAM parameters using the Taguchi method, it was possible to increase the shear strengths by 30% as compared to those of the trial laminates. Further improvements were achieved by artificial neural network (ANN) modeling. As a result, the use of the 50 µm thick extra resin layer made it possible to increase the shear strengths up to 50% relative to those of the trial laminates at a CF content of 70 wt.%. This improvement was achieved via minimizing the number of defects at the interlaminar interfaces. The dependences of both mechanical and structural characteristics of the laminates on the UAM parameters were essentially nonlinear. For their analysis and optimization of the UAM parameters, the direct propagation neural networks with the minimal architecture were utilized. Under the ultra-small sample conditions, the use of a priori knowledge enabled us to predict the results rather accurately.
在本研究中,通过热成型(TF)和超声增材制造(UAM)方法制备了基于聚醚酰亚胺(PEI)且碳纤维(CF)含量为55至70 wt.%的层压板。与TF层压板相比,CF含量高于55 wt.%的UAM层压板的剪切强度低40%,这是由于预浸料中粘结剂的量不足以形成可靠的层间连接。为了提高CF含量为70 wt.%的层压板的剪切强度,使其达到TF层压板的水平,沉积了厚度为50、100和150μm的额外树脂层。通过使用田口方法对UAM参数进行排序,与试验层压板相比,剪切强度提高了30%。通过人工神经网络(ANN)建模实现了进一步的改进。结果,在CF含量为70 wt.%时,使用50μm厚的额外树脂层可使剪切强度相对于试验层压板提高50%。这种改进是通过最小化层间界面处的缺陷数量实现的。层压板的力学和结构特性对UAM参数的依赖性基本上是非线性的。为了分析和优化UAM参数,使用了具有最小架构的直接传播神经网络。在超小样本条件下,利用先验知识能够相当准确地预测结果。