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contributor authorSaquib, Mohammad Nazmus
contributor authorLarson, Richard
contributor authorSattar, Siavash
contributor authorLi, Jiang
contributor authorKravchenko, Sergii G.
contributor authorKravchenko, Oleksandr G.
date accessioned2024-04-24T22:30:55Z
date available2024-04-24T22:30:55Z
date copyright12/11/2023 12:00:00 AM
date issued2023
identifier issn0021-8936
identifier otherjam_91_4_041004.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295359
description abstractA novel approach for microstructure reconstruction using artificial intelligence (MR-AI) was proposed to nondestructively 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 the PPMC plate. Optical microscopy was then used to measure FOD in the cross section of the PPMC plate. FOD measurements from optical microscopy imagery compared favorably with FOD prediction by MR-AI. The proposed methodology opens the opportunity for rapid, nondestructive inspection of manufacturing-induced FOD in molded composites.
publisherThe American Society of Mechanical Engineers (ASME)
titleExperimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite
typeJournal Paper
journal volume91
journal issue4
journal titleJournal of Applied Mechanics
identifier doi10.1115/1.4063983
journal fristpage41004-1
journal lastpage41004-14
page14
treeJournal of Applied Mechanics:;2023:;volume( 091 ):;issue: 004
contenttypeFulltext


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