Gate design algorithm to maximize the fiber orientation effectiveness in thermoplastic injection-molded components
PERIN Mattia, BERTI Guido A., LEE Taeyong, QUAGLIATO Luca
download PDFAbstract. This research presents an automatic algorithm, implemented in a Visual Basic Architecture (VBA), for the optimization of the gate location in thermoplastic injection molding of short fibers reinforced composite materials. The algorithm receives, as input, the geometry of the component and, according to the user’s choice, defines the injection points grid, and relevant versors, on a pre-constructed mesh, automatically runs the finite volume method (FVM) simulation and exports the fiber orientation tensor (FOT) on each node of the mesh. The nodal coordinate of the part and the relevant FOT are then used as the training dataset for a Gradient Boosting (GB) algorithm for the full correlation between injection gate locations and the resulting fiber orientation distribution (FOD), allowing to define the injection gate configuration better suited to maximize the effectiveness of the reinforcement fibers. By coupling the trained GB algorithm with a finite element method (FEM) simulation it was confirmed that the developed algorithm can predict the influence of the gate location on the FOD and the resulting mechanical performances, improving the stiffness between 3.8% and 32.6%, on simple and complex geometries alike.
Keywords
Injection Molding, Fibers Reinforced Composite (FRC), Injection Gate Location, Fiber Orientation, Machine Learning
Published online 4/19/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: PERIN Mattia, BERTI Guido A., LEE Taeyong, QUAGLIATO Luca, Gate design algorithm to maximize the fiber orientation effectiveness in thermoplastic injection-molded components, Materials Research Proceedings, Vol. 28, pp 321-330, 2023
DOI: https://doi.org/10.21741/9781644902479-35
The article was published as article 35 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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