Fiber Orientation Distribution Predictions for an Injection Molded Venturi-Shaped Part Validated Against Experimental Micro-Computed Tomography Characterization

Quintana, María Camila; Frontini, Patricia María; Arriaga, Aitor; Plank, Bernhard; Major, Zoltán

doi:10.3389/fmats.2020.00169

Cited by 4 publications

(9 citation statements)

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“…The effective measurement volumes were 4 mm × 4 mm x 2.5 mm for regions R1 and R3 and 4 mm × 4 mm x 1 mm for region R2. A more detailed description of the procedure can be found in Quintana et al (2020); Emerson et al (2017); Salaberger (2019).…”

Section: Methodsmentioning

confidence: 99%

“…The verification of the FO of the part at the end of the filling stage of the simulation necessitates FO measurement on produced parts via proper microscopic analysis or by computed tomography images. The latter is widely used in many scientific and engineering investigations and recently became a quasi-standardized tool for the verification (Salaberger et al, 2011;Quintana et al, 2020). Due to the existing previously verified results, the Venturi tube was selected for further analysis in this paper.…”

Section: Figurementioning

confidence: 99%

“…Due to the existing previously verified results, the Venturi tube was selected for further analysis in this paper. The previous work using the 3parameter iARD-RPR equation and a factorial design of experiment was performed and published by (Quintana et al, 2020).…”

Section: Figurementioning

confidence: 99%

“…• Introduction of the optimization procedure for determining the fiber orientation model parameters mentioned above • Comparison of the IISO results with the results of the previous paper by (Quintana et al, 2020) which were generated with the simpler iARD-RPR model…”

Section: Figurementioning

confidence: 99%

“…For each of the above cases an averaged fiber orientation tensor was calculated with the final optimum. To ensure a reasonable comparison, an additional simulation with the parameters from Quintana et al (2020) was performed with the new BLM mesh.…”

Section: Simulationmentioning

confidence: 99%

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Determination of fiber orientation model parameters for injection molding simulations via automated metamodel optimization

et al. 2023

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Injection molded short fiber reinforced components reveal a sound light weight potential with moderate costs and thus are widely used in many demanding engineering applications. The accurate determination of the fiber orientation (FO) is essential for predicting the overall mechanical behavior of discontinuous (short or long, with varying aspect ratio) fiber reinforced composites. The simulation of the FO requires a proper modeling of the hydrodynamics, the closure transformation of the FO tensor and optionally the application of specific correction functions. The determination of parameters for the fiber orientation models commonly used in injection molding simulations is a challenging task because they cannot be determined directly in experiments. Hence, a novel way is shown in our paper to derive these parameters faster, more efficiently and accurately by the usage of an automated metamodel optimization. For this, injection molding simulations were performed iteratively by an optimization program until a minimal deviation error of the simulated parameters was reached. The optimization was performed based on proper computed tomography FO data of selected regions of interest. The new approach was tested for a rotationally symmetric Venturi tube geometry made from short glass fiber reinforced polyamide (PA-GF). The fiber orientation distribution models chosen were the iARD-RPR equation with 3 parameters and the novel anisotropic IISO equation with 5 parameters. It was shown that the optimization method is feasible for the calibration of fiber orientation models. Furthermore, the IISO equation with its 2 additional parameters allowed a more accurate prediction of the fiber orientation distribution, especially of the core layer of the injection molded part.

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Section: Methodsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

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