Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods
Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper prese...
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University of Baghdad, College of Science for Women
2023-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8819 |
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| author | Anton S. Chepurnenko Tatiana N. Kondratieva Ebrahim Al-Wali |
| author_facet | Anton S. Chepurnenko Tatiana N. Kondratieva Ebrahim Al-Wali |
| author_sort | Anton S. Chepurnenko |
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Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid.
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| format | Article |
| id | doaj-art-4f90d4ced8254fd5b625f95833b936c1 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-4f90d4ced8254fd5b625f95833b936c12025-08-20T03:56:14ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862023-12-01206(Suppl.)10.21123/bsj.2023.8819Processing of Polymers Stress Relaxation Curves Using Machine Learning MethodsAnton S. Chepurnenko0Tatiana N. Kondratieva 1Ebrahim Al-Wali2Strength of Materials Department, Don State Technical University, Rostov-on-Don, Russia.Mathematics and Computer Science Department, Don State Technical University, Rostov-on-Don, Russia.Strength of Materials Department, Don State Technical University, Rostov-on-Don, Russia. Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of the study is the generated data sets obtained on the basis of theoretical stress relaxation curves. Tables of initial data for training models for all samples are presented, a statistical analysis of the characteristics of the initial data sets is carried out. The total number of numerical experiments for all samples was 346020 variations. When developing the models, CatBoost artificial intelligence methods were used, regularization methods (Weight Decay, Decoupled Weight Decay Regularization, Augmentation) were used to improve the accuracy of the model, and the Z-Score method was used to normalize the data. As a result of the study, intelligent models were developed to determine the rheological parameters of polymers included in the generalized non-linear Maxwell-Gurevich equation (initial relaxation viscosity, velocity modulus) using generated data sets for the EDT-10 epoxy binder as an example. Based on the results of testing the models, the quality of the models was assessed, graphs of forecasts for trainees and test samples, graphs of forecast errors were plotted. Intelligent models are based on the CatBoost algorithm and implemented in the Jupyter Notebook environment in Python. The constructed models have passed the quality assessment according to the following metrics: MAE, MSE, RMSE, MAPE. The maximum value of model error predictions was 0.86 for the MAPE metric, and the minimum value of model error predictions was 0.001 for the MSE metric. Model performance estimates obtained during testing are valid. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8819Artificial intelligence, CatBoost, Machine learning, Metaheuristics, Polymers, Regularization, Regression, Rheology |
| spellingShingle | Anton S. Chepurnenko Tatiana N. Kondratieva Ebrahim Al-Wali Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods مجلة بغداد للعلوم Artificial intelligence, CatBoost, Machine learning, Metaheuristics, Polymers, Regularization, Regression, Rheology |
| title | Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods |
| title_full | Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods |
| title_fullStr | Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods |
| title_full_unstemmed | Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods |
| title_short | Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods |
| title_sort | processing of polymers stress relaxation curves using machine learning methods |
| topic | Artificial intelligence, CatBoost, Machine learning, Metaheuristics, Polymers, Regularization, Regression, Rheology |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8819 |
| work_keys_str_mv | AT antonschepurnenko processingofpolymersstressrelaxationcurvesusingmachinelearningmethods AT tatianankondratieva processingofpolymersstressrelaxationcurvesusingmachinelearningmethods AT ebrahimalwali processingofpolymersstressrelaxationcurvesusingmachinelearningmethods |