Prediction Of Rheological Parameters Of Polymers By Machine Learning Methods - Info and Reading Options

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<p style="margin:5px 0px;color:rgb(102,102,102);font-family:'PT Sans', sans-serif;font-size:17px;background-color:rgb(255,255,255);"><strong><em>Introduction.</em></strong> All polymer materials and composites based on them are characterized by pronounced rheological properties, the prediction of which is one of the most critical tasks of polymer mechanics. Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. Along with these methods, due to the capability to process data with highly nonlinear dependences between features, machine learning methods such as the <em>k</em>-nearest neighbor method, and the support vector machine (SVM) method, are widely used in related areas. However, these methods have not been applied to the problem discussed in this article before. The objective of the research was to develop a predictive model for evaluating the rheological parameters of polymers using artificial intelligence methods by the example of polyvinyl chloride.</p><p style="margin:5px 0px;color:rgb(102,102,102);font-family:'PT Sans', sans-serif;font-size:17px;background-color:rgb(255,255,255);"><strong><em>Materials and Methods.</em></strong> This paper used <em>k</em>-nearest neighbor method and the support vector machine to determine the rheological parameters of polymers based on stress relaxation curves. The models were trained on synthetic data generated from theoretical relaxation curves constructed using the nonlinear Maxwell-Gurevich equation. The input parameters of the models were the amount of deformation at which the experiment was performed, the initial stress, the stress at the end of the relaxation process, the relaxation time, and the conditional end time of the process. The output parameters included velocity modulus and initial relaxation viscosity coefficient. The models were developed in the Jupyter Notebook environment in Python.</p><p style="margin:5px 0px;color:rgb(102,102,102);font-family:'PT Sans', sans-serif;font-size:17px;background-color:rgb(255,255,255);"><strong><em>Results.</em></strong> New predictive models were built to determine the rheological parameters of polymers based on artificial intelligence methods. The proposed models provided high quality prediction. The model quality metrics in the SVR algorithm were: MAE – 1.67 and 0.72; MSE – 5.75 and 1.21; RMSE – 1.67 and 1.1; MAPE – 8.92 and 7.3 for the parameters of the initial relaxation viscosity and velocity modulus, respectively, with the coefficient of determination <em>R</em>² – 0.98. The developed models showed an average absolute percentage error in the range of 5.9 – 8.9%. In addition to synthetic data, the developed models were also tested on real experimental data for polyvinyl chloride in the temperature range from 20° to 60°C.</p><p style="margin:5px 0px;color:rgb(102,102,102);font-family:'PT Sans', sans-serif;font-size:17px;background-color:rgb(255,255,255);"><strong><em><font style="vertical-align:inherit;"><font style="vertical-align:inherit;">Обсуждение и вывод.</font></font></em></strong> <font style="vertical-align:inherit;"><font style="vertical-align:inherit;"> Апробация разработанных моделей на реальных экспериментальных кривых показала высокое качество их аппроксимации, сравнимое с другими методами. Таким образом,</font><em><font style="vertical-align:inherit;"> алгоритм k</font></em><font style="vertical-align:inherit;"> -ближайшего соседа и SVM могут использоваться для прогнозирования реологических параметров полимеров в качестве альтернативы искусственным нейронным сетям и алгоритму CatBoost, требуя меньших усилий для предварительной настройки. При этом в данном исследовании метод SVM оказался наиболее предпочтительным методом машинного обучения, поскольку он более эффективен при обработке большого количества признаков.</font></font> <em><font style="vertical-align:inherit;"></font></em><font style="vertical-align:inherit;"></font></p>

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