{"title":"贝叶斯框架下受损纤维增强层压板的识别和特征描述","authors":"Valentin Noël, Thomas Rodet, Dominique Lesselier","doi":"10.3233/jae-230140","DOIUrl":null,"url":null,"abstract":"Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics 61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":"1 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework\",\"authors\":\"Valentin Noël, Thomas Rodet, Dominique Lesselier\",\"doi\":\"10.3233/jae-230140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics 61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.\",\"PeriodicalId\":50340,\"journal\":{\"name\":\"International Journal of Applied Electromagnetics and Mechanics\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-02-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Electromagnetics and Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3233/jae-230140\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Electromagnetics and Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3233/jae-230140","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework
Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics 61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.
期刊介绍:
The aim of the International Journal of Applied Electromagnetics and Mechanics is to contribute to intersciences coupling applied electromagnetics, mechanics and materials. The journal also intends to stimulate the further development of current technology in industry. The main subjects covered by the journal are:
Physics and mechanics of electromagnetic materials and devices
Computational electromagnetics in materials and devices
Applications of electromagnetic fields and materials
The three interrelated key subjects – electromagnetics, mechanics and materials - include the following aspects: electromagnetic NDE, electromagnetic machines and devices, electromagnetic materials and structures, electromagnetic fluids, magnetoelastic effects and magnetosolid mechanics, magnetic levitations, electromagnetic propulsion, bioelectromagnetics, and inverse problems in electromagnetics.
The editorial policy is to combine information and experience from both the latest high technology fields and as well as the well-established technologies within applied electromagnetics.