Optical Penetration and "Fingerprinting" Analysis of Automotive Optical Liquid Silicone Components Based on Wavelet Analysis and Multiple Recognizable Performance Evaluation.

Abstract

The residual stress phenomenon in the injection process of an optical lens affects the quality of optical components, and the refractive error caused by geometric errors is the most serious, followed by the degradation of the accuracy and function of optical components. It is very important to ensure that the lens geometry remains intact and the refractive index is low. Therefore, a parameter design method for an optical liquid silicon injection molding was proposed in this study. Wavelet analysis was applied to the noise reduction and feature extraction of the cavity pressure/pressure retaining curve of the injection molding machine, and multiple identifiable performance evaluation methods were used to identify and optimize the parameters of the molding process. Taking an automotive LED lens as an example, Moldex3D simulation software was used to simulate the molding of an LED lens made of LSR material, and two key injection molding factors, melt temperature and V/P switching point, were analyzed and optimized. In this paper, the transmittance and volume shrinkage of LED lenses are taken as quality indexes, and parameters are optimized by setting different V/P switching points and melt temperature schemes. The experimental results show that the residual stress is negatively correlated with transmittance, and the higher the residual stress, the lower the transmittance. Under the optimum process parameters generated by this method, the residual stress of plastic parts is significantly optimized, and the optimization rate is above 15%. In addition, when the V/P switching point is 98 and the melt temperature is 30 °C, the product quality is the best, the volume shrinkage rate is the smallest, and the size is 2.895%, which also means that the carbon emissions are the lowest.

• Authors: Hanjui Chang, Shuzhou Lu, Yue Sun, Guangyi Zhang, Longshi Rao
• Journal: Polymers (Polymers (Basel)) Vol. 15 Issue 1 (Dec 25 2022) ISSN: 2073-4360 [Electronic] Switzerland
• PMID: 36616436 (Publication Type: Journal Article)