手工装配中微小金属件漏装模糊自适应视觉检测方法

Fuzzy adaptive vision inspection method for micro metal parts leakage in manual assembly

针对手工装配作业中微小金属件装配区图像特征易受光照环境影响产生不规则动态变化的问题,定性分析了总体视场及微小金属件装配区灰度分布的影响因素,建立了不同装配特征灰度分布混杂区动态识别数学模型;通过BP神经网络学习总体视场及装配区灰度分布特征与不同装配特征之间的非线性关系,并将网络预测精度转化为灰度分布特征对装配特征的模糊隶属度函数,提出了一套基于灰度混杂区阈值模糊自适应调整的微小金属件漏装视觉检测方法。在某企业所生产的钢琴顶盖螺钉漏装机器视觉检测系统中的成功应用,结果表明所提方法对手工装配作业复杂光环境具有较好的鲁棒性和适应性。

Aiming at the irregular dynamic change of image feature on micro metal parts assembly area caused by complex illumination environment in the manual assembly operation,the factors affecting the gray distribution of the whole field of view and micro metal parts assembly area are qualitatively analyzed,and a mathematical model for the dynamic identification of the gray distribution hybrid area among different assembly features is established. Then the nonlinear relationship between global and local gray distribution characteristics and different assembly features is trained by BP neural network,and the network forecasting accuracy is transformed into the fuzzy membership function of gray distribution to assembly features. Finally based on fuzzy adaptive adjustment of gray level hybrid threshold,a machine vision inspection method for micro metal parts leakage in manual assembly operation is proposed. The successful application of the method proposed in a machine vision inspection system for piano cap screw leakage shows that it has good robustness and adaptability to complex light environment in manual assembly.