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Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III
HIGHLIGHTS: What are the main findings? The connection between cavity pressure, clamping force, and tie bar elongation is revealed and applied. The quality of the product is predicted based on the tie bar elongation, and the method is non-destructive, online, and highly efficient. What is the implic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649150/ https://www.ncbi.nlm.nih.gov/pubmed/37959958 http://dx.doi.org/10.3390/polym15214278 |
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author | Chang, Hanjui Lu, Shuzhou Sun, Yue Wang, Rui |
author_facet | Chang, Hanjui Lu, Shuzhou Sun, Yue Wang, Rui |
author_sort | Chang, Hanjui |
collection | PubMed |
description | HIGHLIGHTS: What are the main findings? The connection between cavity pressure, clamping force, and tie bar elongation is revealed and applied. The quality of the product is predicted based on the tie bar elongation, and the method is non-destructive, online, and highly efficient. What is the implication of the main finding? The NSGA III algorithm can solve the multi-objective optimization problem while making the final obtained Pareto solution as well as being uniformly distributed. LSR lenses with low residual stress and high transmittance can be produced using this method. ABSTRACT: This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95–98%. In summary, this approach not only enables the prediction of the lens’s residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes. |
format | Online Article Text |
id | pubmed-10649150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106491502023-10-31 Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III Chang, Hanjui Lu, Shuzhou Sun, Yue Wang, Rui Polymers (Basel) Article HIGHLIGHTS: What are the main findings? The connection between cavity pressure, clamping force, and tie bar elongation is revealed and applied. The quality of the product is predicted based on the tie bar elongation, and the method is non-destructive, online, and highly efficient. What is the implication of the main finding? The NSGA III algorithm can solve the multi-objective optimization problem while making the final obtained Pareto solution as well as being uniformly distributed. LSR lenses with low residual stress and high transmittance can be produced using this method. ABSTRACT: This study aimed to improve the injection molding quality of LSR material lenses by optimizing the process parameters. To achieve this goal, we employed the population-based optimization algorithm NSGA-III, which can simultaneously optimize multiple objective functions and identify an equilibrium point among them, thereby reducing the time required to find the optimal process parameters. We utilized analysis software to simulate the injection molding process of LSR material lenses, with a specific focus on examining the relationship between tie bar elongation and the optimized process parameters. During the study, we intentionally varied key process parameters, including the melt temperature, holding pressure, and holding time, to analyze their impact on the residual stress of the final product. In order to investigate the intricate relationship between the tie bar yield, injection molding process parameters, and lens residual stress, we installed strain sensors on the tie bar to continuously monitor changes in clamping force throughout the injection molding process. The experimental results showed that both the tie bar force and mold cavity pressure exerted significant influence on residual stresses. By applying the NSGA-III algorithm for optimization, we successfully determined the optimal process parameters, which included a melt temperature of 34.92 °C, a holding pressure of 33.97 MPa, and a holding time of 9.96 s. In comparison to the initially recommended process parameters during the design phase, the optimized parameters led to reductions of 12.98% in clamping force and 47.14% in residual stress. Furthermore, the average transmittance of the actual product remained within the range of 95–98%. In summary, this approach not only enables the prediction of the lens’s residual stress trends based on the tie bar elongation, but also leads to a substantial enhancement of lens quality, characterized by reduced residual stress and improved transmittance through the optimization of process parameters. This methodology can serve as a valuable guide for optimizing real-world injection molding processes. MDPI 2023-10-31 /pmc/articles/PMC10649150/ /pubmed/37959958 http://dx.doi.org/10.3390/polym15214278 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chang, Hanjui Lu, Shuzhou Sun, Yue Wang, Rui Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title | Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title_full | Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title_fullStr | Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title_full_unstemmed | Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title_short | Liquid Silicone Rubber Headlamp Lens Injection Molding Process Optimization Based on Tie Bar Elongation and NSGA III |
title_sort | liquid silicone rubber headlamp lens injection molding process optimization based on tie bar elongation and nsga iii |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649150/ https://www.ncbi.nlm.nih.gov/pubmed/37959958 http://dx.doi.org/10.3390/polym15214278 |
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