Mohammad Nazmus Saquib, Richard Larson, Siavash Sattar, Jiang Li, Sergey Kravchenko, Oleksandr Kravchenko
{"title":"基于深度学习的不连续纤维血小板复合材料微观结构重构实验验证","authors":"Mohammad Nazmus Saquib, Richard Larson, Siavash Sattar, Jiang Li, Sergey Kravchenko, Oleksandr Kravchenko","doi":"10.1115/1.4063983","DOIUrl":null,"url":null,"abstract":"Abstract A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to non-destructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of PPMC plate. Optical microscopy was then used to measure FOD in the cross-section of PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, non-destructive inspection of manufacturing induced FOD in molded composites.","PeriodicalId":54880,"journal":{"name":"Journal of Applied Mechanics-Transactions of the Asme","volume":"18 12","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite\",\"authors\":\"Mohammad Nazmus Saquib, Richard Larson, Siavash Sattar, Jiang Li, Sergey Kravchenko, Oleksandr Kravchenko\",\"doi\":\"10.1115/1.4063983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to non-destructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of PPMC plate. Optical microscopy was then used to measure FOD in the cross-section of PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, non-destructive inspection of manufacturing induced FOD in molded composites.\",\"PeriodicalId\":54880,\"journal\":{\"name\":\"Journal of Applied Mechanics-Transactions of the Asme\",\"volume\":\"18 12\",\"pages\":\"0\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Mechanics-Transactions of the Asme\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4063983\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Mechanics-Transactions of the Asme","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4063983","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite
Abstract A novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to non-destructively measure the through-thickness average stochastic fiber orientation distribution (FOD) in a prepreg platelet molded composite (PPMC) plate. MR-AI approach uses thermal strain components on the surfaces of a PPMC plate as input to the deep learning model, which allows to predict a distribution of local through-thickness average fiber orientation state in the entire PPMC volume. The experimental setup with a heating stage and digital image correlation (DIC) was used to measure thermal strains on the surface of PPMC plate. Optical microscopy was then used to measure FOD in the cross-section of PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, non-destructive inspection of manufacturing induced FOD in molded composites.
期刊介绍:
All areas of theoretical and applied mechanics including, but not limited to: Aerodynamics; Aeroelasticity; Biomechanics; Boundary layers; Composite materials; Computational mechanics; Constitutive modeling of materials; Dynamics; Elasticity; Experimental mechanics; Flow and fracture; Heat transport in fluid flows; Hydraulics; Impact; Internal flow; Mechanical properties of materials; Mechanics of shocks; Micromechanics; Nanomechanics; Plasticity; Stress analysis; Structures; Thermodynamics of materials and in flowing fluids; Thermo-mechanics; Turbulence; Vibration; Wave propagation