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Application of neural network and computer simulation to Improve surface profile of injection molding optic lens

机译:神经网络和计算机仿真在改善注塑光学镜片表面轮廓中的应用

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摘要

A search method using neural network algorithm and computer simulation was proposed to solve multi-variables problems such as injection molding process. In the injection molding of optical lenses, injection conditions have critical effects on the optical quality of molded lens. High-precision injection molding techniques are required for the fabrication of plastic optical lens. Therefore, the suggested technique in this study was constructed to search optimum conditions for improving surface profile of optical lenses through restraining porosity creation and minimizing thickness reduction. Simulation for injection molding was conducted focusing on the influence of various process parameters on defects such as porosity creation and thickness reduction. After the simulation, optimum injection molding conditions were predicted using a neural network program based on leaning data extracted from simulation results. To demonstrate the effectiveness of this technique, a series of injection molding experiments were carried out, and experimental results under selected injection conditions were compared with the results output predicted by the neural network.
机译:提出了一种使用神经网络算法和计算机仿真的搜索方法来解决注塑成型等多变量问题。在光学透镜的注射成型中,注射条件对成型透镜的光学质量有关键影响。塑料光学透镜的制造需要高精度的注射成型技术。因此,本研究中建议的技术旨在通过限制孔隙率的产生和最小化厚度减小来寻找最佳条件,以改善光学镜片的表面轮廓。着重于各种工艺参数对缺陷的影响(例如,孔隙率的产生和厚度的减小),进行了注塑成型仿真。模拟之后,基于从模拟结果中提取的倾斜数据,使用神经网络程序预测最佳注射成型条件。为了证明该技术的有效性,进行了一系列注射成型实验,并将所选注射条件下的实验结果与神经网络预测的结果进行了比较。

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