[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126712-en":3,"doc-seo-126712-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126712,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods - Research Overview","The study develops machine learning models to determine the rheological (viscoelastic) properties of polymers directly from experimental stress relaxation curves. It surveys metaheuristic directions such as local search and evolutionary algorithms for combinatorial optimization, emphasizing decision-tree construction, and performs a comparative regression analysis using CatBoost Regressor. Generated datasets based on theoretical relaxation curves are used, with training tables and statistical characterization provided. Accuracy improvements rely on CatBoost methods plus regularization (Weight Decay, Decoupled Weight Decay, augmentation) and Z-Score normalization. Model quality is evaluated via MAE, MSE, RMSE, and MAPE, with errors validated in testing and exemplified for an EDT-10 epoxy binder. ","Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods  \nAnton S. Chepurnenko *1, Tatiana N. Kondratieva 2, Ebrahim Al-Wali 1  \n1Strength of Materials Department, Don State Technical University, Rostov-on-Don, Russia.  \n2Mathematics and Computer Science Department, Don State Technical University, Rostov-on-Don, Russia.  \n*Corresponding author  \nReceived 29/03/2023, Revised 09/06/2023, Accepted 11/06/2023, Published 05/12/2023  \n This work is licensed under a Creative Commons Attribution 4.0 International License.  \nAbstract  \nCurrently, 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 presentsan 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 arepresented, 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 ofthe 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.  \nKeywords: Artificial intelligence, CatBoost, Machine learning, Metaheuristics, Polymers, Regularization, Regression, Rheology.  \nIntroduction  \nCurrently, polymeric materials and composites based on them are increasingly used in construction and other industries. As with traditional building materials such as wood and concrete, polymeric  \nmaterials are characterized by a pronounced creep phenomenon. Solving the problem of polymer mechanics is impossible without determining their rheological properties. The rheological behavior of  \npolymeric materials can be described by linear 1 and nonlinear models2-5. Nonlinear models are more complex, but at the same time provide better agreement with experimental data. One of the simplest linear models is the Maxwell-Thompson model, in which the body is represented as a combination of viscous and elastic elements. Introduction to this model by G.I. Gurevich, the dependence of the relaxation viscosity of the polymer on stress made it possible to obtain good agreement with experiment for many polymers6–9. In the case ofa uniaxial stress state, the basic Eq. of the Maxwell-Gurevich model has the form10 :  ","cbCaionS3o2069sN","https://ap.wps.com/l/cbCaionS3o2069sN","pdf",1246196,1,10,"English","en",105,"# Introduction\n## Rheological properties and the Maxwell-Gurevich model\n## Challenges in extracting relaxation viscosity and velocity modulus\n## Machine learning approaches and CatBoost Regressor","[{\"question\":\"What is the main goal of the paper on polymer stress relaxation curves?\",\"answer\":\"To develop machine learning models that infer polymer rheological properties from experimental stress relaxation curves, using generated datasets based on theoretical curves.\"},{\"question\":\"Why does the work focus on the Maxwell-Gurevich model parameters?\",\"answer\":\"Because key rheological parameters—initial relaxation viscosity and velocity modulus—are difficult to obtain simultaneously since the corresponding theoretical creep/relaxation curves lack analytical forms.\"},{\"question\":\"How are the machine learning models built and evaluated?\",\"answer\":\"CatBoost-based models are trained in a Python Jupyter Notebook environment, with regularization and Z-Score normalization. Performance is assessed using MAE, MSE, RMSE, and MAPE on training and test samples.\"}]","Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods - Research Overview | PDF",1785934348,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"processing-of-polymers-stress-relaxation-curves-using-machine-learning-methods-research-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/processing-of-polymers-stress-relaxation-curves-using-machine-learning-methods-research-overview/126712/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the paper on polymer stress relaxation curves?","Question",{"text":75,"@type":76},"To develop machine learning models that infer polymer rheological properties from experimental stress relaxation curves, using generated datasets based on theoretical curves.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the work focus on the Maxwell-Gurevich model parameters?",{"text":80,"@type":76},"Because key rheological parameters—initial relaxation viscosity and velocity modulus—are difficult to obtain simultaneously since the corresponding theoretical creep/relaxation curves lack analytical forms.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models built and evaluated?",{"text":84,"@type":76},"CatBoost-based models are trained in a Python Jupyter Notebook environment, with regularization and Z-Score normalization. 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