An anisotropic rotary diffusion model for fiber orientation in short- and long-fiber thermoplastics

Phelps, Jay H.; Tucker, Charles L.

doi:10.1016/j.jnnfm.2008.08.002

Cited by 282 publications

(209 citation statements)

References 18 publications

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“…However, the Phan-Thien et al model's diffusion term failed to return the fibers to an isotropic orientation at steady state, a necessary condition of any diffusion model. To correct the Phan-Thien et al model, an expression for rotary diffusion was developed that was defined on the surface of the unit sphere traced by all orientations of the unit vector p [1][2]. The expression for the ARD model to properly match the LFT fiber orientation data is: (1) where A and are the second-and fourth-order orientation tensors, respectively.…”

Section: The Phelps-tucker Anisotropic Rotary Diffusion -Reduced Stramentioning

confidence: 99%

“…Tensor C is constructed from the A and D tensors as: (2) where b i (i=1,..,5) are the scalar constants. A systematic method of selecting b i was developed in [1][2] to ensure stable and valid orientation solutions. To properly match experimental orientation data, it is necessary to slow the predicted orientation dynamics of the ARD model.…”

Section: The Phelps-tucker Anisotropic Rotary Diffusion -Reduced Stramentioning

confidence: 99%

“…The ARD-RSC model contains a set of material parameters ( i b and RSC parameters) which can be identified using a target orientation state [1]. In principle, the target orientation state in the steady simple shear flow is chosen for the identification.…”

Section: Identification Of the Ard-rsc Model Parametersmentioning

confidence: 99%

“…A careful selection of the five parameters b i is crucial in order to obtain accurate and stable fiber orientation solution. A valid set of parameters b i must satisfy the criteria proposed in [1] and must give a solution to either the ARD or ARD-RSC model in which…”

Section: Identification Of the Ard-rsc Model Parametersmentioning

confidence: 99%

“…PNNL/260), Battelle PNNL had worked with the University of Illinois at Urbana-Champaign (UIUC) through subcontracts to develop advanced process models for fiber orientation and length distributions in injection-molded LFTs [1][2][3]. These models are the Phelps-Tucker anisotropic rotary diffusion -reduced strain closure (ARD-RSC) model for predicting fiber orientation and the Phelps-Tucker fiber length attrition model.…”

Section: Introductionmentioning

confidence: 99%

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Validation of New Process Models for Large Injection-Molded Long-Fiber Thermoplastic Composite Structures

Nguyen¹,

Jin²,

Wang³

et al. 2012

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Executive SummaryRecently, long-fiber thermoplastic (LFT) composites have attracted great interest within the automotive industry since these materials offer much better structural performance (e.g. higher elastic moduli, strength, and durability) than their short-fiber analogues, and they can be processed through injection molding with some specific tool design. However, to be able to compute the properties of injection molded LFTs, there is an important need to develop process models and computational tools to predict the microstructure of these composites. The microstructure of injection molded LFTs is governed by i) flow-induced fiber orientation, ii) fiber breakage during injection molding, and iii) processing conditions (e.g. pressure, mold and melt temperatures, mold geometries, injection speed, etc.). Under the Cooperative Research and Development Agreement (CRADA) Nr. PNNL/260 between Battelle as Operator of Pacific Northwest National Laboratory (PNNL) and Autodesk, Inc. (hereinafter "Autodesk"), process models developed by the University of Illinois for predicting fiber length and orientation distributions in injection-molded LFTs were implemented in the research versions of Autodesk Moldflow Insight (AMI) software packages (hereinafter "Moldflow"). These models were then used in the Moldflow injection molding simulations of simple and small-scale long-glassfiber/polypropylene samples.

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Section: The Phelps-tucker Anisotropic Rotary Diffusion -Reduced Stramentioning

confidence: 99%

Section: The Phelps-tucker Anisotropic Rotary Diffusion -Reduced Stramentioning

confidence: 99%

Section: Identification Of the Ard-rsc Model Parametersmentioning

confidence: 99%

Section: Identification Of the Ard-rsc Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of New Process Models for Large Injection-Molded Long-Fiber Thermoplastic Composite Structures

Nguyen¹,

Jin²,

Wang³

et al. 2012

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Multiphysics for Simulation of Forming Processes

Silva¹,

Laure²,

Coupez³

et al. 2016

Heat Transfer in Polymer Composite Materials

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This chapter is dedicated to the simulation of composite forming processes. It concerns numerical methods for molding, at the different material scales, involving multiphysics analysis. From the different possible numerical techniques, we focus on a stabilized finite element method for the resolution of the continuum equations (mass, momentum and energy conservation), in an Eulerian framework, with a level-set approach for interface displacement treatment. Internal variables, such as the reticulation rate or fiber fraction and orientation may be computed using models defined through ordinary differential equations. Examples at the process scale, but also at the composite inner structure scales illustrate the capabilities of such numerical techniques.

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Modeling and Optimization of the Injection‐Molding Process: A Review

et al. 2016

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ABSTRACT:The purpose of this article is to review the research done in the field of mathematical modeling and optimization of the injection-molding (IM) process. Various papers related to the mathematical description of the filling, postfilling, and plasticating phases of the IM process were assessed, and some recent advances on the IM field are described. In addition, research devoted to the optimization of the IM process based on various techniques is also discussed. These optimization techniques include design of experiments, artificial neural networks, and evolutionary algorithms. The strengths and weaknesses of each approach were discussed. Finally, this paper also discusses the optimization research performed in the IM process regarding some of the specific features associated with the processes such as runner system and cooling channel configurations, process conditions, gate location, and cavity pressure balancing. C

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An anisotropic rotary diffusion model for fiber orientation in short- and long-fiber thermoplastics

Cited by 282 publications

References 18 publications

Validation of New Process Models for Large Injection-Molded Long-Fiber Thermoplastic Composite Structures

Validation of New Process Models for Large Injection-Molded Long-Fiber Thermoplastic Composite Structures

Multiphysics for Simulation of Forming Processes

Modeling and Optimization of the Injection‐Molding Process: A Review

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