Academic Journal
Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process
| العنوان: | Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process |
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| المؤلفون: | Perin M., Lim Y., Berti G. A., Lee T., Jin K., Quagliato L. |
| المساهمون: | Perin, M., Lim, Y., Berti, G. A., Lee, T., Jin, K., Quagliato, L. |
| بيانات النشر: | MDPI |
| سنة النشر: | 2023 |
| المجموعة: | Padua Research Archive (IRIS - Università degli Studi di Padova) |
| مصطلحات موضوعية: | fiber orientation optimization, gradient boosting (GB), injection gate design, injection molding, machine learning (ML), short fiber reinforced composite (SFRC), stiffness optimization |
| الوصف: | Fiber reinforcement orientation in thermoplastic injection-molded components is both a strength as well as a weak point of this largely employed manufacturing process. Optimizing the fiber orientation distribution (FOD) considering the shape of the part and the applied loading conditions allows for enhancing the mechanical performances of the produced parts. Henceforth, this research proposes an algorithm to identify the best injection gate (IG) location/s starting from a 3D model and a user-defined load case. The procedure is composed of a first Visual Basic Architecture (VBA) code that automatically sets and runs Finite Volume Method (FVM) simulations to find the correlation between the fiber orientation tensor (FOT) and the IG locations considering single and multiple gates combinations up to three points. A second VBA code elaborates the results and builds a dataset considering the user-defined loading and constraint conditions, allowing the assignment of a score to each IG solution. Three geometrical components of increasing complexity were considered for a total of 1080 FVM simulations and a total computational time of similar to 390 h. The search for the best IG location has been further expanded by training a Machine Learning (ML) model based on the Gradient Boosting (GB) algorithm. The training database (DB) is based on FVM simulations and was expanded until a satisfactory prediction accuracy higher than 90% was achieved. The enhancement of the local FOD on the critical regions of three components was verified and showed an average improvement of 26.9% in the stiffness granted by a high directionality of the fibers along the load path. Finite element method (FEM) simulations and laboratory experiments on an industrial pump housing, injection-molded with a polyamide-66 reinforced with 30% of short glass fibers (PA66-30GF) material were also carried out to validate the FVM-FEM simulation frame and showed a 16.4% local stiffness improvement in comparison to the currently employed IG solution. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | ELETTRONICO |
| اللغة: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/pmid/37514484; info:eu-repo/semantics/altIdentifier/wos/WOS:001075766900001; volume:15; issue:14; numberofpages:24; journal:POLYMERS; https://hdl.handle.net/11577/3504748; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85166296692 |
| DOI: | 10.3390/polym15143094 |
| الاتاحة: | https://hdl.handle.net/11577/3504748 https://doi.org/10.3390/polym15143094 |
| رقم الانضمام: | edsbas.B344E4E8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/polym15143094 |
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