Simulation of Short-Fiber-Reinforced Polymers

A special issue of Fibers (ISSN 2079-6439).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 10948

Special Issue Editor

Leibniz-Institute of Polymer Research Dresden e.V., Hohe Str. 6, 01069 Dresden, Germany
Interests: engineering with polymers; short-fiber-reinforced polymers; process simulation; structural simulation; dimensioning; material characterization; material modelling

Special Issue Information

Dear Colleagues,

For many technical applications, plastics offer an extended degree of freedom in design, economical production, and high durability. A very important class of polymer materials are thermoplastics reinforced by short fibers (SFRP). SFRP have excellent properties in many respects, but set extremely high demands for product development:

  • In contrast to many engineering materials, the material properties of SFRP parts are not given a priori. They only result from a close interaction between material, processing, and part design, since the short fibers of SFRP in the part are oriented differently in space locally due to the melt flows during processing by injection molding.
  • While conventional engineering materials can be considered with adequate accuracy as a continuum, in the case of SFRP, the multi-phase morphology of the material at the micro level must often be known at each spatial position in the part’s volume, and accordingly must be considered during the part’s development.
  • The thermoplastic matrix of an SFRP possesses a manifold thermo-mechanical material behavior and can only be described as a linear elastic material to a very limited extent. As polyamides are used in the matrix of many SFRPs, their moisture-dependent material behavior is an additional challenging factor.

In order to meet the requirements of engineering for a comprehensive numerical description of SFRPs, extensive research activities have been carried out over the last several decades. The increased performance of numerical simulation tools on the one hand, and especially a deeper understanding of SFRPs and holistic modelling approaches on the other, have contributed to the development of simulation tools that allow much more realistic results for SFRPs.

This Special Issue "Simulation of Short-Fiber-Reinforced Polymers" intends to cover recent advances in simulating SFRPs and using the simulation results obtained appropriately. It addresses contributions from researchers working in the fields of process and structural simulations as well as component design/dimensioning and material characterization of SFRPs.

Prof. Dr. Markus Stommel
Guest Editor

Manuscript Submission Information

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Keywords

  • short-fiber-reinforced plastics
  • material modelling
  • dimensioning
  • fatigue prediction
  • fiber orientation
  • multiscale simulation
  • process simulation
  • structural simulation
  • injection molding
  • polymer testing

Published Papers (3 papers)

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Research

16 pages, 10705 KiB  
Article
Micromechanical Modeling of Anisotropy and Strain Rate Dependence of Short-Fiber-Reinforced Thermoplastics
by Shaokang Zhang, Johannes A. W. van Dommelen and Leon E. Govaert
Fibers 2021, 9(7), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/fib9070044 - 02 Jul 2021
Cited by 5 | Viewed by 2778
Abstract
The anisotropy and strain rate dependence of the mechanical response of short-fiber-reinforced thermoplastics was studied using a straightforward micromechanical finite element analysis of representative volume elements (RVEs). RVEs are created based on the fiber orientation tensor, which quantifies the processing-induced fiber orientation distribution. [...] Read more.
The anisotropy and strain rate dependence of the mechanical response of short-fiber-reinforced thermoplastics was studied using a straightforward micromechanical finite element analysis of representative volume elements (RVEs). RVEs are created based on the fiber orientation tensor, which quantifies the processing-induced fiber orientation distribution. The matrix is described by a strain rate-dependent constitutive model (the Eindhoven glassy polymer (EGP) model), which accurately captures the intrinsic response of amorphous polymers. The micromechanical results indicate that the influence of strain rate and that of the loading direction on the yield stress are multiplicatively decouplable, which confirms previous experimental observations. Moreover, it is demonstrated that the yield stress, to a good approximation, can be directly linked to the fiber orientation in the direction of loading. This leads to a new relation that uniquely links the rate dependence of the yield stress to the fiber orientation in loading direction. Full article
(This article belongs to the Special Issue Simulation of Short-Fiber-Reinforced Polymers)
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17 pages, 5055 KiB  
Article
Statistical Analysis of Mechanical Stressing in Short Fiber Reinforced Composites by Means of Statistical and Representative Volume Elements
by Kevin Breuer, Axel Spickenheuer and Markus Stommel
Fibers 2021, 9(5), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/fib9050032 - 06 May 2021
Cited by 9 | Viewed by 2999
Abstract
Analyzing representative volume elements with the finite element method is one method to calculate the local stress at the microscale of short fiber reinforced plastics. It can be shown with Monte-Carlo simulations that the stress distribution depends on the local arrangement of the [...] Read more.
Analyzing representative volume elements with the finite element method is one method to calculate the local stress at the microscale of short fiber reinforced plastics. It can be shown with Monte-Carlo simulations that the stress distribution depends on the local arrangement of the fibers and is therefore unique for each fiber constellation. In this contribution the stress distribution and the effective composite properties are examined as a function of the considered volume of the representative volume elements. Moreover, the influence of locally varying fiber volume fraction is examined, using statistical volume elements. The results show that the average stress probability distribution is independent of the number of fibers and independent of local fluctuation of the fiber volume fraction. Furthermore, it is derived from the stress distributions that the statistical deviation of the effective composite properties should not be neglected in the case of injection molded components. A finite element analysis indicates that the macroscopic stresses and strains on component level are significantly influenced by local, statistical fluctuation of the composite properties. Full article
(This article belongs to the Special Issue Simulation of Short-Fiber-Reinforced Polymers)
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14 pages, 4938 KiB  
Article
Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database
by Kevin Breuer and Markus Stommel
Fibers 2021, 9(2), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/fib9020008 - 01 Feb 2021
Cited by 31 | Viewed by 4399
Abstract
In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The [...] Read more.
In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The fiber volume fraction, fiber length, matrix-phase properties, and fiber orientation are varied so that the neural network can be used within a very wide range of parameters. A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network. The average prediction accuracy is equal or better than by a two-step homogenization using the classical method of Mori and Tanaka. Moreover, it is shown that the training of the neural network on an extended data set works well and that particularly calculation-intensive data points can be avoided without loss of prediction quality. Full article
(This article belongs to the Special Issue Simulation of Short-Fiber-Reinforced Polymers)
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