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Title: Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm

Abstract

Fiber-reinforced polymer composites are strong candidates for structural materials to replace steel and light alloys in lightweight vehicle design because of their low density and relatively high strength. In the integrated computational materials engineering (ICME) development of carbon fiber composites, microstructure reconstruction algorithms are needed to generate material microstructure representative volume element (RVE) based on the material processing information. The microstructure RVE reconstruction enables the material property prediction by finite element analysis (FEA)This paper presents an algorithm to reconstruct the microstructure of a chopped carbon fiber/epoxy laminate material system produced by compression molding, normally known as sheet molding compounds (SMC). The algorithm takes the result from material’s manufacturing process as inputs, such as the orientation tensor of fibers, the chopped fiber sheet geometry, and the fiber volume fraction. The chopped fiber sheets are treated as deformable rectangle chips and a random packing algorithm is developed to pack these chips into a square plate. The RVE is built in a layer-by-layer fashion until the desired number of lamina is reached, then a fine tuning process is applied to finalize the reconstruction. Compared to the previous methods, this new approach has the ability to model bended fibers by allowing limited amount ofmore » overlaps of rectangle chips. Furthermore, the method does not need SMC microstructure images, for which the image-based characterization techniques have not been mature enough, as inputs. Case studies are performed and the results show that the statistics of the reconstructed microstructures generated by the algorithm matches well with the target input parameters from processing.« less

Authors:
; ;
Publication Date:
Research Org.:
Ford Motor Company, Detroit, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1431227
DOE Contract Number:  
EE0006867
Resource Type:
Conference
Resource Relation:
Conference: WCX17: SAE World Congress Experience
Country of Publication:
United States
Language:
English

Citation Formats

Huang, Tianyu, Xu, Hongyi, and Chen, Wei. Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm. United States: N. p., 2017. Web.
Huang, Tianyu, Xu, Hongyi, & Chen, Wei. Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm. United States.
Huang, Tianyu, Xu, Hongyi, and Chen, Wei. Thu . "Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm". United States. https://www.osti.gov/servlets/purl/1431227.
@article{osti_1431227,
title = {Microstructure Reconstruction of Sheet Molding Composite Using a Random Chips Packing Algorithm},
author = {Huang, Tianyu and Xu, Hongyi and Chen, Wei},
abstractNote = {Fiber-reinforced polymer composites are strong candidates for structural materials to replace steel and light alloys in lightweight vehicle design because of their low density and relatively high strength. In the integrated computational materials engineering (ICME) development of carbon fiber composites, microstructure reconstruction algorithms are needed to generate material microstructure representative volume element (RVE) based on the material processing information. The microstructure RVE reconstruction enables the material property prediction by finite element analysis (FEA)This paper presents an algorithm to reconstruct the microstructure of a chopped carbon fiber/epoxy laminate material system produced by compression molding, normally known as sheet molding compounds (SMC). The algorithm takes the result from material’s manufacturing process as inputs, such as the orientation tensor of fibers, the chopped fiber sheet geometry, and the fiber volume fraction. The chopped fiber sheets are treated as deformable rectangle chips and a random packing algorithm is developed to pack these chips into a square plate. The RVE is built in a layer-by-layer fashion until the desired number of lamina is reached, then a fine tuning process is applied to finalize the reconstruction. Compared to the previous methods, this new approach has the ability to model bended fibers by allowing limited amount of overlaps of rectangle chips. Furthermore, the method does not need SMC microstructure images, for which the image-based characterization techniques have not been mature enough, as inputs. Case studies are performed and the results show that the statistics of the reconstructed microstructures generated by the algorithm matches well with the target input parameters from processing.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {4}
}

Conference:
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