TY - GEN
T1 - Gate design algorithm to maximize the fiber orientation effectiveness in thermoplastic injection-molded components
AU - Perin, Mattia
AU - Berti, Guido A.
AU - Lee, Taeyong
AU - Quagliato, Luca
N1 - Funding Information:
This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-5199990614253).
Publisher Copyright:
© 2023, Association of American Publishers. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Fiber Orientation
KW - Fibers Reinforced Composite (FRC)
KW - Injection Gate Location
KW - Injection Molding
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85160225694&partnerID=8YFLogxK
U2 - 10.21741/9781644902479-35
DO - 10.21741/9781644902479-35
M3 - Conference contribution
AN - SCOPUS:85160225694
SN - 9781644902462
T3 - Materials Research Proceedings
SP - 321
EP - 330
BT - Material Forming - The 26th International ESAFORM Conference on Material Forming – ESAFORM 2023
A2 - Madej, Lukasz
A2 - Sitko, Mateusz
A2 - Perzynsk, Konrad
PB - Association of American Publishers
T2 - 26th International ESAFORM Conference on Material Forming, ESAFORM 2023
Y2 - 19 April 2023 through 21 April 2023
ER -