{"title":"机器学习在牙科材料磨损预测中的应用","authors":"A. Suryawanshi, N. Behera","doi":"10.32381/jpm.2023.40.3-4.11","DOIUrl":null,"url":null,"abstract":"Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest model show an MAE of 0.7011, 0.0773, 0.0771 and 0.2199. AdaBoost model performs poorly in comparison to other models.","PeriodicalId":50083,"journal":{"name":"Journal of Polymer Materials","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Machine Learning For Prediction Dental Material Wear\",\"authors\":\"A. Suryawanshi, N. Behera\",\"doi\":\"10.32381/jpm.2023.40.3-4.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest model show an MAE of 0.7011, 0.0773, 0.0771 and 0.2199. AdaBoost model performs poorly in comparison to other models.\",\"PeriodicalId\":50083,\"journal\":{\"name\":\"Journal of Polymer Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Polymer Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.32381/jpm.2023.40.3-4.11\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"POLYMER SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Polymer Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.32381/jpm.2023.40.3-4.11","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"POLYMER SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
树脂复合材料通常用作牙科修复材料。这些材料的磨损是一个主要问题。在这项研究中,牙科复合材料制成的试样在针盘摩擦仪中进行了体外测试。实验中使用的四种不同的牙科复合材料在咀嚼烟草溶液中浸泡数天后取出,进行磨损测试。随后,四种不同的机器学习(ML)算法(AdaBoost、CatBoost、Gradient Boosting、Random Forest)被用于开发牙科材料磨损预测模型。AdaBoost、CatBoost、梯度提升和随机森林模型的 MAE 分别为 0.7011、0.0773、0.0771 和 0.2199。与其他模型相比,AdaBoost 模型表现较差。
Application of Machine Learning For Prediction Dental Material Wear
Resin composites are commonly applied as the material for dental restoration. Wear of these materials is a major issue. In this study specimens made of dental composite materials were subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials applied in the experiment were soaked in a solution of chewing tobacco for certain days before being removed and put through a wear test. Subsequently, four different machine learning (ML) algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient Boosting and Random Forest model show an MAE of 0.7011, 0.0773, 0.0771 and 0.2199. AdaBoost model performs poorly in comparison to other models.
期刊介绍:
Journal of Polymer Materials-An International Journal is published quarterly (4 issues per year), which covers broadly most of the important and fundamental areas of Polymer Science and Technology. It reports reviews on current topics and original research results on synthesis of monomers and polymers, polymer analysis, characterization and testing, properties of polymers, structure-property relation, polymer processing and fabrication, and polymer applications. Research and development activities on functional polymers, polymer blends and alloys, composites and nanocomposites, paints and surface coatings, rubbers and elastomeric materials, and adhesives are also published.