Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet CompositeSource: Journal of Applied Mechanics:;2023:;volume( 091 ):;issue: 004::page 41004-1Author:Saquib, Mohammad Nazmus
,
Larson, Richard
,
Sattar, Siavash
,
Li, Jiang
,
Kravchenko, Sergii G.
,
Kravchenko, Oleksandr G.
DOI: 10.1115/1.4063983Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A 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.
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contributor author | Saquib, Mohammad Nazmus | |
contributor author | Larson, Richard | |
contributor author | Sattar, Siavash | |
contributor author | Li, Jiang | |
contributor author | Kravchenko, Sergii G. | |
contributor author | Kravchenko, Oleksandr G. | |
date accessioned | 2024-04-24T22:30:55Z | |
date available | 2024-04-24T22:30:55Z | |
date copyright | 12/11/2023 12:00:00 AM | |
date issued | 2023 | |
identifier issn | 0021-8936 | |
identifier other | jam_91_4_041004.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295359 | |
description abstract | A 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite | |
type | Journal Paper | |
journal volume | 91 | |
journal issue | 4 | |
journal title | Journal of Applied Mechanics | |
identifier doi | 10.1115/1.4063983 | |
journal fristpage | 41004-1 | |
journal lastpage | 41004-14 | |
page | 14 | |
tree | Journal of Applied Mechanics:;2023:;volume( 091 ):;issue: 004 | |
contenttype | Fulltext |