In this paper, to improve the optical quality of aspherical plastic lenses for mobile use, the optimal molding conditions that can minimize the phase difference are derived using injection molding simulation, design of experiments, and machine learning. First, factors affecting the phase difference were derived using the design of the experiment method, and a data set was created using the derived factors, followed by the machine learning process. After predicting the model trained using the generated training data as test data and verifying it with the performance evaluation index, the model with the best predictive performance was the random forest model. Therefore, to derive the optimal molding conditions, random forests were used to predict 10,000 random pieces of data. As a result of applying the derived optimal molding conditions to the injection molding simulation, the phase difference of the lens could be reduced by 8.2%.

The cooling process in the injection molding requires the longest time. Therefore, a lot of studies have been conducted to reduce the cooling time. In particular, studies on conformal cooling channels using 3D printing are actively being conducted. In this study, the effect of the conformal cooling channel considering the hood shape instead of the conventional linear cooling channel was investigated by injection molding analysis. In the analysis results, when the conformal cooling channel was applied, the length deformation of the molded product was reduced by about 33% and the circular deformation of the hood assembled on the lens was reduced by about 7.1㎛.