AI Article Synopsis
- The study addresses the low yield rate of large, irregular glass components in vehicles caused by stress and dimension deviations during glass molding.
- A numerical model was created to analyze molding quality factors, particularly focusing on the impact of molding temperature and pressure on energy consumption and dimensional accuracy.
- The optimization algorithm NSGA-II identified the best molding conditions, which were experimentally verified to have a predictive error under 20%, confirming the model's effectiveness for future precision molding applications.
Article Abstract
The high level of stress and dimension deviation induced by glass molding are the main causes of the low yield rate of large, irregular glass components on vehicles. To solve this issue, a numerical model of large glass component molding was established in this study, which aimed to analyze the dominant factors of molding quality and achieve a synergistic balance between quality characteristics and energy consumption. The results show that molding temperature is the dominant factor affecting the energy consumption and residual stress, and the molding pressure is the main factor affecting the dimension deviation. Furthermore, the NSGA-II optimization algorithm was used to optimize the maximum residual stress, dimension deviation, and energy consumption with the numerical results. The combination of a heating rate of 1.95 °C/s, holding time of 158 s, molding temperature of 570 °C, molding pressure of 34 MPa, and cooling rate of 1.15 °C/s was determined to be the optimized scheme. The predictive error of the numerical result, based on the optimized scheme, was experimentally verified to be less than 20%. It proved the accuracy of the model in this study. These results can provide guidance for the subsequent precision molding of large, irregular glass components.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10609172 | PMC |
| http://dx.doi.org/10.3390/mi14101974 | DOI Listing |
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