First Advisor
Faryar Etesami
Date of Award
Summer 2021
Document Type
Thesis
Degree Name
Bachelor of Science (B.S.) in Mechanical Engineering and University Honors
Department
Mechanical and Materials Engineering
Language
English
Subjects
Injection molding of plastics, Three-dimensional printing, Additive manufacturing
DOI
10.15760/honors.1163
Abstract
The applicability of hollow infill patterns has been explored for its applications in making 3D printed polymer-based injection molds in the additive manufacturing industry. Hollow infill patterns offer a significant reduction in material costs as well as the opportunity for reducing the cooling times via pumping a coolant fluid through the hollow cavity in a similar fashion to traditional conformal cooling channels. A 3D Jacks Support Hollow mold model was determined to be the best performing design. FEA analysis was conducted to determine the maximum reduction in internal volume (percentage of material saved) that could be achieved without exceeding the acceptable stress and deflection limitations for the 3D Jacks Supports Hollow mold design compared to the traditional base solid mold model used in injection molding. The transient thermal simulations were performed to determine the effects of cooldown times between the base solid mold model and the hollow 3D Jacks Supports model. Finally, real flow (transient conjugate heat transfer) simulation was performed in order to determine a more accurate result pertaining to the cooldown time for the best performing polymer in this study (30% carbon reinforced PEEK). The results show a 62% in material saved for reinforced PEEK compared to the base solid mold and a cooling time reduction of 93.9%. A transient conjugate heat transfer flow analysis indicates a 27% difference between results when compared to a simplified transient thermal analysis.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/36252
Recommended Citation
Fong, Alan, "Optimization of 3D Printed Mold Performance for Injection Molding via Hollow Infill Patterns" (2021). University Honors Theses. Paper 1132.
https://doi.org/10.15760/honors.1163