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Title: Uncertainty quantification of fiber orientation distribution measurements for long-fiber-reinforced thermoplastic composites

Abstract

Here, we present a detailed methodology for experimental measurement of fiber orientation distribution in injection-molded discontinuous fiber composites using the method of ellipses on two-dimensional cross sections. Best practices to avoid biases occurring during surface preparation and optical imaging of carbon-fiber-reinforced thermoplastics are discussed. We developed a marker-based watershed transform routine for efficient image segmentation and the separation of touching fiber ellipses. The sensitivity of the averaged orientation tensor to the image sample size is studied for the case of long-fiber thermoplastics. A Mori–Tanaka implementation of the Eshelby model is then employed to quantify the sensitivity of elastic stiffness predictions to biases in the fiber orientation distribution measurements.

Authors:
ORCiD logo [1];  [1];  [2]; ORCiD logo [3]; ORCiD logo [1]
  1. Purdue Univ., West Lafayette, IN (United States). Chemical Engineering. Shool of Aeronautics and Astronautics
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Univ. of Illinois, Urbana-Champaign, IL (United States). Dept. of Mechanical Science and Engineering
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1411878
Report Number(s):
PNNL-SA-122781
Journal ID: ISSN 0021-9983
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Composite Materials
Additional Journal Information:
Journal Name: Journal of Composite Materials; Journal ID: ISSN 0021-9983
Publisher:
SAGE
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; long-fiber thermoplastics; injection molding; fiber orientation distribution measurement; uncertainty quantification; elastic properties

Citation Formats

Sharma, Bhisham N., Naragani, Diwakar, Nguyen, Ba Nghiep, Tucker, Charles L., and Sangid, Michael D. Uncertainty quantification of fiber orientation distribution measurements for long-fiber-reinforced thermoplastic composites. United States: N. p., 2017. Web. doi:10.1177/0021998317733533.
Sharma, Bhisham N., Naragani, Diwakar, Nguyen, Ba Nghiep, Tucker, Charles L., & Sangid, Michael D. Uncertainty quantification of fiber orientation distribution measurements for long-fiber-reinforced thermoplastic composites. United States. doi:10.1177/0021998317733533.
Sharma, Bhisham N., Naragani, Diwakar, Nguyen, Ba Nghiep, Tucker, Charles L., and Sangid, Michael D. Thu . "Uncertainty quantification of fiber orientation distribution measurements for long-fiber-reinforced thermoplastic composites". United States. doi:10.1177/0021998317733533.
@article{osti_1411878,
title = {Uncertainty quantification of fiber orientation distribution measurements for long-fiber-reinforced thermoplastic composites},
author = {Sharma, Bhisham N. and Naragani, Diwakar and Nguyen, Ba Nghiep and Tucker, Charles L. and Sangid, Michael D.},
abstractNote = {Here, we present a detailed methodology for experimental measurement of fiber orientation distribution in injection-molded discontinuous fiber composites using the method of ellipses on two-dimensional cross sections. Best practices to avoid biases occurring during surface preparation and optical imaging of carbon-fiber-reinforced thermoplastics are discussed. We developed a marker-based watershed transform routine for efficient image segmentation and the separation of touching fiber ellipses. The sensitivity of the averaged orientation tensor to the image sample size is studied for the case of long-fiber thermoplastics. A Mori–Tanaka implementation of the Eshelby model is then employed to quantify the sensitivity of elastic stiffness predictions to biases in the fiber orientation distribution measurements.},
doi = {10.1177/0021998317733533},
journal = {Journal of Composite Materials},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 28 00:00:00 EDT 2017},
month = {Thu Sep 28 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on September 28, 2018
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