神经网络模型在开发涂胶工作站中的应用
Application of Neural Networks Model in Developing Glue-Coating Workstation

本文基于车灯涂胶工艺和统计过程控制的思想，对胶量克重系统进行分析，评估了测量系统的稳定性，并确定了涂胶工艺的评价基准。基于数据建模的思想和BP神经网络模型，通过分析工艺过程参数之间的相关性，搭建了胶量克重和工艺过程参数之间的数据模型。利用在线编程，实现了涂胶工作站对胶量克重的在线预测。为保证涂胶产品质量(防止涂胶缺陷)，对涂胶产品克重进行管理，实现了在线反馈调节功能。
Based on the idea of car lamp gluing process and statistical process control, this article analyzes the gluing weight system, evaluates the stability of the measurement system, and determines the evaluation benchmark of the gluing process. Based on the idea of data modeling and BP neural networks model, a data model between gluing weight and process parameters is built by analyzing the correlation between process parameters. Online programming is used to achieve online prediction of gluing weight in the gluing workstation. In order to ensure the quality of gluing products (prevent gluing defects), the gluing weight is managed and the online feedback adjustment function is realized.