基于机器视觉的自动裁切机精定位算法研究
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摘要
针对传统自动裁切机因裁切精度较低无法满足高精度产品要求的问题,通过对裁切机结构进行研究,提出了利用机器视觉技术来提高裁切机裁切精度的方法。首先利用改进的形态学梯度滤波算子找到板料的粗略边缘,然后采用灰度矩法对之进行亚像素精定位,利用最小二乘法将符合要求的亚像素边缘点拟合成一条直线,再根据几何关系计算出板料偏移量,指导纠偏平台进行偏差补偿,最后利用Matlab对该方法进行了图像边缘定位的仿真测试。研究结果表明:改进后的裁切机裁切精度理论上可达0. 03 mm,满足绝大多数高精度产品的精度要求。
Aiming at the problem that the requirements for high-precision products could not be satisfied due to the low cutting precision of traditional automatic cutting machines,a method of using machine vision technology to improve the cutting precision of the cutting machine was proposed by studying the structure of the cutting machine. Firstly,the improved morphological gradient filter operator was used to find the rough edge of the sheet,and then the sub-pixel fine positioning was performed by the gray scale method. Finally,the sub-pixel edge points of meeting the requirements were fitted into a straight line by least square method. Then the offset of the sheet was calculated according to the geometric relationship,and was used to guide the correction platform to compensate the deviation. The method was simulated and tested by image edge positioning experiment designed by Matlab. The results indicate that the cutting precision of the traditional automatic cutting machine can reach 0. 03 mm theoretically,which meets the precision requirements of most high-precision products.
Aiming at the problem that the requirements for high-precision products could not be satisfied due to the low cutting precision of traditional automatic cutting machines,a method of using machine vision technology to improve the cutting precision of the cutting machine was proposed by studying the structure of the cutting machine. Firstly,the improved morphological gradient filter operator was used to find the rough edge of the sheet,and then the sub-pixel fine positioning was performed by the gray scale method. Finally,the sub-pixel edge points of meeting the requirements were fitted into a straight line by least square method. Then the offset of the sheet was calculated according to the geometric relationship,and was used to guide the correction platform to compensate the deviation. The method was simulated and tested by image edge positioning experiment designed by Matlab. The results indicate that the cutting precision of the traditional automatic cutting machine can reach 0. 03 mm theoretically,which meets the precision requirements of most high-precision products.
引文
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[3] PARK J Y,KANG H,LEE J W,et al. A study on rock cutting efficiency and structural stability of a point attack pick cutter by lab-scale linear cutting machine testing and finite element analysis[J]. International Sciences,2018,103(8):32-37.
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[5]张素枝,秦宇锦.基于主动视觉的相机标定方法在太阳能电池片丝印机上的应用[J].机电工程,2016,33(3):378-382.
[6]张美静,石振刚.改进形态学梯度的样条插值亚像素边缘检测方法[J].沈阳理工大学学报,2012,31(3):19-23.
[7]曾亚君,何毅斌,李伟.基于改进形态学梯度的齿轮边缘检测[J].工具技术,2017,51(1):101-103.
[8] ZHU Zi-min,LIU Rong qiang. Image edge detection based on Solitary wave algorithm[J]. Applied Mechanics and Materials,2014,3277(608):81-94.
[9] FABIJANSKA A. A survey of subpixel edge detection methods for images of heat-emitting metal specimens[J]. International Journal of Applied Mathematics and Computer Science,2012,22(3):65-68.
[10]周敬,王俊元,张彬.基于亚像素边缘检测的刀具几何参数测量[J].工具技术,2010,44(9):94-97.
[11]谌湘倩,刘珂,马飞.基于协作表示和正则化最小二乘法的多姿态人脸识别方法[J].现代电子技术,2017,40(5):49-53.
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