ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES
This research analyzes the potential and prospects of a data-driven methodology for examining the thermo-physical properties of composite materials. The research is to provide an analysis of the potential and benefits of employing data-driven procedures, especially in contrast to conventional method...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
National Technical University Kharkiv Polytechnic Institute
2024-12-01
|
| Series: | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
| Subjects: | |
| Online Access: | http://samit.khpi.edu.ua/article/view/320130 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841554363781218304 |
|---|---|
| author | Ruslan Lavshchenko Gennadiy Lvov |
| author_facet | Ruslan Lavshchenko Gennadiy Lvov |
| author_sort | Ruslan Lavshchenko |
| collection | DOAJ |
| description | This research analyzes the potential and prospects of a data-driven methodology for examining the thermo-physical properties of composite materials. The research is to provide an analysis of the potential and benefits of employing data-driven procedures, especially in contrast to conventional methods. The analysis examines fundamental principles and advanced machine learning approaches utilized in materials science, highlighting their ability to improve the knowledge, optimization, and overall quality of composite materials. This study thoroughly examines the application of neural networks in forecasting thermal characteristics, highlighting its predictive skills and potential to transform the analysis of thermal properties in composite materials. Additionally, the research underscores the growing reliance on big data analytics in addressing complex challenges in material behavior, particularly under variable environmental conditions. A comparison assessment is performed between the data-driven methodology and traditional analytical methodologies, emphasizing the distinct advantages and drawbacks of each. This comparison elucidates how data-driven methodologies can enhance and refine the precision of thermo-physical analysis. The convergence of machine learning and material science is shown to not only facilitate more accurate predictions but also reduce experimentation time and costs. The report also delineates contemporary techniques for measuring and forecasting the thermo-physical properties of composites, emphasizing the advancements in new technologies in recent years. The function of computational tools and computer technology is elaborated upon, especially with the modeling of thermo-physical properties and the simulation of production processes for composite materials. This paper highlights the growing significance of these technologies in enhancing both theoretical and practical dimensions of material science. The research provides novel insights into composite manufacture, thereby advancing the future of materials science and the practical applications of composite materials. The results have significant implications for enhancing production processes, fostering innovation, and progressing the research of composite materials across diverse industries. |
| format | Article |
| id | doaj-art-d93db2b06c9946d1ac6857950d7e3863 |
| institution | Kabale University |
| issn | 2079-0023 2410-2857 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | National Technical University Kharkiv Polytechnic Institute |
| record_format | Article |
| series | Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології |
| spelling | doaj-art-d93db2b06c9946d1ac6857950d7e38632025-01-08T14:40:15ZengNational Technical University Kharkiv Polytechnic InstituteВісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології2079-00232410-28572024-12-012 (12)111710.20998/2079-0023.2024.02.02358791ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITESRuslan Lavshchenko0https://orcid.org/0009-0001-3649-1118Gennadiy Lvov1https://orcid.org/0000-0003-0297-9227National Technical University "Kharkiv Polytechnic Institute"National Technical University "Kharkiv Polytechnic Institute"This research analyzes the potential and prospects of a data-driven methodology for examining the thermo-physical properties of composite materials. The research is to provide an analysis of the potential and benefits of employing data-driven procedures, especially in contrast to conventional methods. The analysis examines fundamental principles and advanced machine learning approaches utilized in materials science, highlighting their ability to improve the knowledge, optimization, and overall quality of composite materials. This study thoroughly examines the application of neural networks in forecasting thermal characteristics, highlighting its predictive skills and potential to transform the analysis of thermal properties in composite materials. Additionally, the research underscores the growing reliance on big data analytics in addressing complex challenges in material behavior, particularly under variable environmental conditions. A comparison assessment is performed between the data-driven methodology and traditional analytical methodologies, emphasizing the distinct advantages and drawbacks of each. This comparison elucidates how data-driven methodologies can enhance and refine the precision of thermo-physical analysis. The convergence of machine learning and material science is shown to not only facilitate more accurate predictions but also reduce experimentation time and costs. The report also delineates contemporary techniques for measuring and forecasting the thermo-physical properties of composites, emphasizing the advancements in new technologies in recent years. The function of computational tools and computer technology is elaborated upon, especially with the modeling of thermo-physical properties and the simulation of production processes for composite materials. This paper highlights the growing significance of these technologies in enhancing both theoretical and practical dimensions of material science. The research provides novel insights into composite manufacture, thereby advancing the future of materials science and the practical applications of composite materials. The results have significant implications for enhancing production processes, fostering innovation, and progressing the research of composite materials across diverse industries.http://samit.khpi.edu.ua/article/view/320130data-driven approachcompositesthermo-physical propertiesdata analysismathematical modelingmachine learningprocess optimizationsimulations |
| spellingShingle | Ruslan Lavshchenko Gennadiy Lvov ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES Вісник Національного технічного університету "ХПÌ": Системний аналіз, управління та інформаційні технології data-driven approach composites thermo-physical properties data analysis mathematical modeling machine learning process optimization simulations |
| title | ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES |
| title_full | ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES |
| title_fullStr | ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES |
| title_full_unstemmed | ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES |
| title_short | ANALYSIS OF THE APPLICATIONS OF THE DATA-DRIVEN APPROACH IN EVALUATING THE THERMAL-PHYSICAL PROPERTIES OF COMPOSITES |
| title_sort | analysis of the applications of the data driven approach in evaluating the thermal physical properties of composites |
| topic | data-driven approach composites thermo-physical properties data analysis mathematical modeling machine learning process optimization simulations |
| url | http://samit.khpi.edu.ua/article/view/320130 |
| work_keys_str_mv | AT ruslanlavshchenko analysisoftheapplicationsofthedatadrivenapproachinevaluatingthethermalphysicalpropertiesofcomposites AT gennadiylvov analysisoftheapplicationsofthedatadrivenapproachinevaluatingthethermalphysicalpropertiesofcomposites |