Cavity balance improvement for injection molded parts via automated flow leader generation
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Felipe Porcher
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Paul Borger
Abstract
Better injection molded part quality can be achieved with a more balanced filling of the single mold cavity. Furthermore, a more homogeneous cavity pressure distribution results in less overpacked regions contributing to an overall lower pressure difference between gate locations and end of fill regions (Lam, Y. and Seow, L.W. 2000). Different process design parameters can affect the melt filling pattern inside the cavity, most notably the gate location, injection speed as well as geometrical constrains and accelerators, known as flow deflectors and flow leaders, respectively. However, determining its geometric parameters such as path, length, and cross-section is not a straightforward task. To address this problem, we developed an automated flow leader generation routine that uses injection molding simulations to determine the longest flow path along which the thickness of the part will be gradually increased, ultimately reducing the overall melt flow resistance. Our flow leader generation approach is based on the work of Seow, L. and Lam, Y.C. (1997) and Lam, Y. and Seow, L.W. (2000). However, in contrast to the principle used by Lam and Seow, the thicker portion of our flow leader is near the injection location, thus exploiting the higher injection pressure. We applied our approach to a demonstrator part to keep track of the fill pattern improvement. The highest fill time difference on the part’s boundaries, called fill time delta, was chosen as a measure for the cavity balance. To validate our method, an injection molding tool for the demonstrator part was manufactured and experiments were performed. We manufactured one unmodified cavity and one flow leader cavity generated with our method. Finally, we were able to demonstrate that our automated flow leader generation method improved the cavity balance both in simulation as well as in experiments, while simultaneously reducing the maximum injection pressure.
Acknowledgments
The authors would like to extend their gratitude to Gino Wybranietz, Konstantin Jakob and Bartlomiej Piotrowski from the BSH Hausgeräte GmbH, as well as Oliver Löschke from the PTK department at TU Berlin.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: LLM, AI or MLT tools where used with the sole purpose of improving language.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Editorial
- PPS2024 Ferrol: advances and perspectives in polymer processing
- Research Articles
- Applying network theory to the modeling of multilayer flows in slot dies: a use case for symbolic regression-based co-extrusion prediction models
- Multiscale polyethylene fiber – bacterial nanocellulose composites through combined laser fusion and bacterial in situ synthesis
- Novel approach to produce reinforced plastic weld seams using an additive friction stir welding process
- Local thermal activation for a combined thermoforming and 3D-printing process
- A new recycling strategy for airbag waste
- Highly electro-conductive PEDOT based thermoplastic composites: effect of filler form factor on electrical percolation threshold
- Cavity balance improvement for injection molded parts via automated flow leader generation
- Application of artificial intelligence techniques to select the objectives in the multi-objective optimization of injection molding
- Modeling melt conveying and power consumption of conveying elements in co-rotating twin-screw extruders
Articles in the same Issue
- Frontmatter
- Editorial
- PPS2024 Ferrol: advances and perspectives in polymer processing
- Research Articles
- Applying network theory to the modeling of multilayer flows in slot dies: a use case for symbolic regression-based co-extrusion prediction models
- Multiscale polyethylene fiber – bacterial nanocellulose composites through combined laser fusion and bacterial in situ synthesis
- Novel approach to produce reinforced plastic weld seams using an additive friction stir welding process
- Local thermal activation for a combined thermoforming and 3D-printing process
- A new recycling strategy for airbag waste
- Highly electro-conductive PEDOT based thermoplastic composites: effect of filler form factor on electrical percolation threshold
- Cavity balance improvement for injection molded parts via automated flow leader generation
- Application of artificial intelligence techniques to select the objectives in the multi-objective optimization of injection molding
- Modeling melt conveying and power consumption of conveying elements in co-rotating twin-screw extruders
