基于图形识别的数控机床误差溯因方法

针对数控机床误差溯因方法复杂且适应性差的问题,提出了基于数控系统圆检测图形的特征角点分布规律,并与神经网络相结合实现对机床运动误差的快速溯因方法。首先,提取所生成圆图形的特征角点,构造反向间隙、周期误差等特征矩阵;然后结合神经网络将低维特征矩阵映射至高维特征空间,实现对数控机床误差的快速溯因。实验表明,该方法简单有效,误差识别准确率较高,且具有较强的通用性。

Method for CNC Machine Tool's Motion Error Abduction Based on Graphic Recognition

For motion error abduction in CNC machine tools, complicated mathematical models are often needed to detect specific motion error for a specific CNC machine tool in the literature. A convenient method to simplify motion error abduction was proposed. The corner distribution on a divided error circle image generated by numerical control system was detected and the RBF neural network was combined to recognize motion error. Firstly, a new corner which indicated the distance from circle curve to circle center saltation of 16-piece divided circle was defined. The average radius and corner number of every piece of the divided circle were put into characteristic matrix to represent error circle images. In order to verify the performance of the characteristic matrix, SVM was employed to test five types of motion error circle images, which turned out to be an excellent solution with high recognition accuracy. Lastly, a RBF neural network was applied to recognize the motion errors by using the characteristic matrix as input and every motion error as output. Results show that the proposed method is of high efficiency and good accuracy for motion error abduction.