Arsen Abdulali, Ibragim R. Atadjanov, Seungkyu Lee, Seokhee Jeon
{"title":"Measurement-based Hyper-elastic Material Identification and Real-time FEM Simulation for Haptic Rendering","authors":"Arsen Abdulali, Ibragim R. Atadjanov, Seungkyu Lee, Seokhee Jeon","doi":"10.1145/3359996.3364275","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a measurement-based modeling framework for hyper-elastic material identification and real-time haptic rendering. We build a custom data collection setup that captures shape deformation and response forces during compressive deformation of cylindrical material samples. We collected training and testing sets of data from four silicone objects having various material profiles. We design an objective function for material parameter identification by incorporating both shape deformation and reactive forces and utilize a genetic algorithm. We adopted an optimization-based Finite Element Method (FEM) for object deformation rendering. The numerical error of simulated forces was found to be perceptually negligible.","PeriodicalId":393864,"journal":{"name":"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3359996.3364275","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
In this paper, we propose a measurement-based modeling framework for hyper-elastic material identification and real-time haptic rendering. We build a custom data collection setup that captures shape deformation and response forces during compressive deformation of cylindrical material samples. We collected training and testing sets of data from four silicone objects having various material profiles. We design an objective function for material parameter identification by incorporating both shape deformation and reactive forces and utilize a genetic algorithm. We adopted an optimization-based Finite Element Method (FEM) for object deformation rendering. The numerical error of simulated forces was found to be perceptually negligible.