The use of neural network models is currently a trend. These models are used in a number of fields, particularly in practice-oriented scientific research. Neural network models allow us to predict results and determine the effectiveness of potential solutions.<\/p>\n
Our research team uses these models for the complex processing of polymer composite materials (PCMs) produced using additive manufacturing technologies and consisting of continuous carbon fiber and a superstructural polymer, polyetheretherketone. The processing is performed in a microwave field. Applying this processing to fully fabricated PCMs improves their physical and mechanical properties. This is achieved through a number of simultaneous processes, including heating the sample, intense heating at the matrix-fiber interface, localized melting of the polymer, and others. The complexity of the experimental work precludes the collection of a large amount of empirical data. Therefore, we use a series of models interconnected in a specific manner. We train them using available data and validate them using one of the quality metrics: MSE, NRMSE, and R\u00b2. The coefficient of determination (R\u00b2) characterizes the proportion of variance in the dependent variable explained by the model.<\/p>\n
The analysis of a series of models yielded R\u00b2 results ranging from 0.87 to 0.99. These values characterize the high quality of the resulting models and indicate the absence of overfitting.<\/p>\n
The work was supported by the Russian Science Foundation grant 23-79-00039.<\/p>\n","protected":false},"featured_media":5383,"template":"","meta":{"_acf_changed":false},"schedule":[7],"speaker-category":[10],"class_list":["post-5371","speaker","type-speaker","status-publish","has-post-thumbnail","hentry","schedule-day-2","speaker-category-poster"],"acf":[],"yoast_head":"\n