高速电弧放电加工电极损耗视觉测量系统关键算法研究

Development of Key Algorithms for Vision Measurement in Blasting Erosion Arc Machining

高速电弧放电加工过程中,电极会在蚀除工件材料的同时发生径向和轴向损耗,从而导致加工尺寸精度差、加工余量不均匀等问题,且在加工中存在大量非放电空走刀时间,影响加工效率。为避免出现上述情况以及提升电弧加工工艺的成熟度,需对包括损耗长度及损耗圆角半径在内的损耗量进行测量并针对所加工的几何特征采取相应的电极损耗补偿策略。利用机器视觉技术对电极损耗量进行测量,着重研究了损耗圆角(非标准圆形)特征的准确识别提取难题,提出一种基于轮廓的非标准圆的自适应识别算法。结果表明:该算法对比其他同类算法具有更加快速准确、鲁棒性更强的特点,满足电弧加工电极损耗测量的要求,具有广泛的应用前景。

During high-speed electrical arc machining,the electrode wears gradually in both radial and axial directions,and the tool wear will lead to poor dimensional accuracy and long non-discharge traveling time,greatly influence the efficiency. Therefore,in order to avoid the above situation and to improve the maturity of the arc machining process,it is necessary to measure the loss amount including the length loss and the radius loss first,and apply a compensation strategy for tool wears. In this paper,the tool wear is measured by machine vision technology,and problems in accurate recognition and extraction of loss rounded corner(non-standard circular) features is studied. An adaptive recognition algorithm based on contours for non-standard circles is proposed. The algorithm has the characteristics of faster and more accurate and strong robustness, meets the requirements of arc machining electrode loss measurement,and has broad application prospects.