Stepanov Dmitry Y, Tian Defang, Alexenko Vladislav O, Panin Sergey V, Buslovich Dmitry G
Laboratory of Mechanics of Polymer Composite Materials, Institute of Strength Physics and Materials Science of Siberian Branch of Russian Academy of Sciences, 634055 Tomsk, Russia.
Department of Materials Science, Engineering School of Advanced Manufacturing Technologies, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.
Polymers (Basel). 2024 Feb 6;16(4):451. doi: 10.3390/polym16040451.
The aim of this study was to optimize the ultrasonic consolidation (USC) parameters for 'PEI adherend/Prepreg (CF-PEI fabric)/PEI adherend' lap joints. For this purpose, artificial neural network (ANN) simulation was carried out. Two ANNs were trained using an ultra-small data sample, which did not provide acceptable predictive accuracy for the applied simulation methods. To solve this issue, it was proposed to artificially increase the learning sample by including additional data synthesized according to the knowledge and experience of experts. As a result, a relationship between the USC parameters and the functional characteristics of the lap joints was determined. The results of ANN simulation were successfully verified; the developed USC procedures were able to form a laminate with an even regular structure characterized by a minimum number of discontinuities and minimal damage to the consolidated components.
本研究的目的是优化“聚醚酰亚胺被粘物/预浸料（碳纤维 - 聚醚酰亚胺织物）/聚醚酰亚胺被粘物”搭接接头的超声固结（USC）参数。为此，进行了人工神经网络（ANN）模拟。使用超小数据样本训练了两个ANN，这对于所应用的模拟方法而言并未提供可接受的预测精度。为解决此问题，建议根据专家的知识和经验，通过纳入额外合成的数据来人为增加学习样本。结果，确定了USC参数与搭接接头功能特性之间的关系。ANN模拟结果得到了成功验证；所开发的USC工艺能够形成具有均匀规则结构的层压板，其特征在于间断最少且对固结部件的损伤最小。
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