Mohammad Nazmus Saquib, Richard Larson, Siavash Sattar, Jiang Li, Sergey Kravchenko, Oleksandr Kravchenko
{"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}
引用次数: 0
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.
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