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This study focuses on obtaining the injection molding parameter settings for producing high precision double-sided aspherical lenses. A neural network approach is used to construct a quality predictor, and a genetic algorithm is followed to search for the optimal parameters settings of the predictor. Mold temperature, plastic temperature and holding pressure are selected as the major process parameters in experiments. At first, Taguchi method is carried out to find the optimal parameter combination for each side of the lens. However, the optimμm parameter combinations obtained for two feature surfaces are not the same. Therefore, multi-objective Taguchi method is used to identify a preferred parameter combination for the two surfaces. The obtained parameter combination is then used as the training set for a neural network quality predictor. Regression analysis is conducted by using predicted results and test results to realize the performance of the quality predictor. Finally, a genetic algorithm is applied to obtain a set of optimal combination of parameters which can improve the accuracy of double-sided aspherical lenses. Results of optimization parameters obtained by multi-objective Taguchi method are mold temperature 110 ℃, plastic temperature 250 ℃, and holding pressure 65MPa. The surface shape accuracy of double-sided aspherical lens for feature surface I is 1.874μm, and 2.181μm for feature surface II. The optimization parameters obtained by quality predictor and genetic algorithms are mold temperature 100.6 ℃, plastic temperature 251.3 ℃, and holding pressure 57.2MPa. The surface shape accuracy of double-sided aspherical lens for feature surface I is 1.221μm, and 0.968μm for feature surface II. Comparing the obtained results, parameter settings obtained by using the optimization method combining quality predictor with genetic algorithm can improve the surface accuracy of the double-sided aspherical lens.
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