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Title: Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses

Abstract

This project proposed to integrate, optimize and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk Simulation Moldflow Insight (ASMI) package for injection-molded long-carbon-fiber thermoplastic composites into a cohesive prediction capability. The current effort focused on rendering the developed models more robust and efficient for automotive industry part design to enable weight savings and cost reduction. The project goal has been achieved by optimizing the developed models, improving and integrating their implementations in ASMI, and validating them for a complex 3D LCF thermoplastic automotive part (Figure 1). Both PP and PA66 were used as resin matrices. After validating ASMI predictions for fiber orientation and fiber length for this complex part against the corresponding measured data, in collaborations with Toyota and Magna PNNL developed a method using the predictive engineering tool to assess LCF/PA66 complex part design in terms of stiffness performance. Structural three-point bending analyses of the complex part and similar parts in steel were then performed for this purpose, and the team has then demonstrated the use of stiffness-based complex part design assessment to evaluate weight savings relative to the body system target (≥ 35%) set in Table 2 of DE-FOA-0000648 (AOI #1).more » In addition, starting from the part-to-part analysis, the PE tools enabled an estimated weight reduction for the vehicle body system using 50 wt% LCF/PA66 parts relative to the current steel system. Also, from this analysis an estimate of the manufacturing cost including the material cost for making the equivalent part in steel has been determined and compared to the costs for making the LCF/PA66 part to determine the cost per “saved” pound.« less

Authors:
 [1];  [1];  [2];  [3];  [4]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Toyota Research Inst. North America, Ann Arbor, MI (United States)
  3. MAGNA Exteriors and Interiors Corporation, Aurora, ON (Canada)
  4. PlastiComp, Inc., Winona, MN (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1399185
Report Number(s):
PNNL-25646
830403000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; long carbon fiber thermoplastic; Fiber Orientation; fiber length; three-point bending; Weight Reduction; Cost Analysis

Citation Formats

Nguyen, Ba Nghiep, Fifield, Leonard S., Gandhi, Umesh N., Mori, Steven, and Wollan, Eric J. Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses. United States: N. p., 2016. Web. doi:10.2172/1399185.
Nguyen, Ba Nghiep, Fifield, Leonard S., Gandhi, Umesh N., Mori, Steven, & Wollan, Eric J. Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses. United States. https://doi.org/10.2172/1399185
Nguyen, Ba Nghiep, Fifield, Leonard S., Gandhi, Umesh N., Mori, Steven, and Wollan, Eric J. Mon . "Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses". United States. https://doi.org/10.2172/1399185. https://www.osti.gov/servlets/purl/1399185.
@article{osti_1399185,
title = {Predictive Engineering Tools for Injection-Molded Long-Carbon-Thermoplastic Composites: Weight and Cost Analyses},
author = {Nguyen, Ba Nghiep and Fifield, Leonard S. and Gandhi, Umesh N. and Mori, Steven and Wollan, Eric J.},
abstractNote = {This project proposed to integrate, optimize and validate the fiber orientation and length distribution models previously developed and implemented in the Autodesk Simulation Moldflow Insight (ASMI) package for injection-molded long-carbon-fiber thermoplastic composites into a cohesive prediction capability. The current effort focused on rendering the developed models more robust and efficient for automotive industry part design to enable weight savings and cost reduction. The project goal has been achieved by optimizing the developed models, improving and integrating their implementations in ASMI, and validating them for a complex 3D LCF thermoplastic automotive part (Figure 1). Both PP and PA66 were used as resin matrices. After validating ASMI predictions for fiber orientation and fiber length for this complex part against the corresponding measured data, in collaborations with Toyota and Magna PNNL developed a method using the predictive engineering tool to assess LCF/PA66 complex part design in terms of stiffness performance. Structural three-point bending analyses of the complex part and similar parts in steel were then performed for this purpose, and the team has then demonstrated the use of stiffness-based complex part design assessment to evaluate weight savings relative to the body system target (≥ 35%) set in Table 2 of DE-FOA-0000648 (AOI #1). In addition, starting from the part-to-part analysis, the PE tools enabled an estimated weight reduction for the vehicle body system using 50 wt% LCF/PA66 parts relative to the current steel system. Also, from this analysis an estimate of the manufacturing cost including the material cost for making the equivalent part in steel has been determined and compared to the costs for making the LCF/PA66 part to determine the cost per “saved” pound.},
doi = {10.2172/1399185},
url = {https://www.osti.gov/biblio/1399185}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {8}
}