V. Kulyk, I. Izonin, V. Vavrukh, R. Tkachenko, Z. Duriagina, B. Vasyliv, M. Kováčová
{"title":"PREDICTION OF HARDNESS, FLEXURAL STRENGTH, AND FRACTURE TOUGHNESS OF ZRO2 BASED CERAMICS USING ENSEMBLE LEARNING ALGORITHMS","authors":"V. Kulyk, I. Izonin, V. Vavrukh, R. Tkachenko, Z. Duriagina, B. Vasyliv, M. Kováčová","doi":"10.36547/ams.29.2.1819","DOIUrl":null,"url":null,"abstract":"Flexural strength, hardness, and fracture toughness are the basic mechanical properties of ceramic materials. Manufacturers widely use this set of properties to ensure the durability of ceramic products. However, many factors, such as chemical and phase compositions, sintering temperature, average grain size, density, and others, affect these properties, making it challenging to estimate corresponding reliability parameters correctly. Experimental examination of the impact of these factors on the mechanical properties of ceramics is a rather time-consuming and resource-consuming procedure. This work aims to predict the mechanical properties of zirconia ceramics using machine learning tools. The authors have created an experimental database for predicting the hardness, flexural strength, and fracture toughness of ZrO2-based ceramics based on chemical composition, phase composition, microstructural features, and sintering temperature on the mechanical properties of zirconia ceramics. The authors compare compared the effectiveness of using different machine learning algorithms and have found a high accuracy of the predicted values of each of the three mechanical properties using boosting ensemble methods. Also they developed a stacked ensemble of machine learning methods to improve the accuracy of determining the hardness property prediction task. We obtained the increase in accuracy of more than 10% (R2) using our approach.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36547/ams.29.2.1819","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Flexural strength, hardness, and fracture toughness are the basic mechanical properties of ceramic materials. Manufacturers widely use this set of properties to ensure the durability of ceramic products. However, many factors, such as chemical and phase compositions, sintering temperature, average grain size, density, and others, affect these properties, making it challenging to estimate corresponding reliability parameters correctly. Experimental examination of the impact of these factors on the mechanical properties of ceramics is a rather time-consuming and resource-consuming procedure. This work aims to predict the mechanical properties of zirconia ceramics using machine learning tools. The authors have created an experimental database for predicting the hardness, flexural strength, and fracture toughness of ZrO2-based ceramics based on chemical composition, phase composition, microstructural features, and sintering temperature on the mechanical properties of zirconia ceramics. The authors compare compared the effectiveness of using different machine learning algorithms and have found a high accuracy of the predicted values of each of the three mechanical properties using boosting ensemble methods. Also they developed a stacked ensemble of machine learning methods to improve the accuracy of determining the hardness property prediction task. We obtained the increase in accuracy of more than 10% (R2) using our approach.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.