Verification of turning insert specifications through three-dimensional vision system

Most computer numerically controlled (CNC) machine tools with turning capability use turning tools with clamped inserts due to their high precision and ease of replacement. However, although the handles of the turning tools are marked with their specification details, such labels do not appear on the inserts themselves and, thus, often lead to misplacement and installation of incorrect inserts. Accordingly, many researchers have proposed imaging systems based on scanners, single cameras, or microscopes for the automatic measurement and identification of inserts. However, such systems require that the inserts be unloaded from the turning tool and positioned precisely in front of the imaging system. Consequently, online measurement is impossible. This study thus proposes a three-dimensional (3D) vision system capable of identifying inserts in situ based on 3D measurements. Specifications such as insert angles, edge lengths, and nose radii of each insert were identified. The feasibility of the proposed system is demonstrated by identifying the specifications of nine types of insert. The experimental results show that the system achieves an average recognition rate of 98.89% for insert angles, 95.56% for cutting edge lengths, and 92.22% for nose radii.

[1]  Fernando Seco Granja,et al.  A Real-Time Tool Positioning Sensor for Machine-Tools , 2009, Sensors.

[2]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[3]  Stamatis Vassiliadis,et al.  A sum of absolute differences implementation in FPGA hardware , 2002, Proceedings. 28th Euromicro Conference.

[4]  Xi Zhang,et al.  Automatic Segmentation of the Apparent Contour for 3D Modeling of Cutting Tools from Single View , 2008, ISVC.

[5]  Didier Demigny,et al.  Evaluation of edge detectors performances with a discrete expression of Canny's criteria , 1995, Proceedings., International Conference on Image Processing.

[6]  Y. K. Chou,et al.  Tool nose radius effects on finish hard turning , 2004 .

[7]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[8]  Mani Maran Ratnam,et al.  Edge detection and measurement of nose radii of cutting tool inserts from scanned 2-D images , 2012 .

[9]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[10]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Mahmudur Rahman,et al.  A study on the effect of tool nose radius in ultrasonic elliptical vibration cutting of tungsten carbide , 2009 .

[12]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[13]  Mani Maran Ratnam,et al.  Determination of tool nose radii of cutting inserts using machine vision , 2011 .

[14]  Kazuo Yamazaki,et al.  A study on three-dimensional vision system for machining setup verification , 2010 .

[15]  Anselmo Eduardo Diniz,et al.  Cutting conditions for finish turning process aiming: the use of dry cutting , 2002 .

[16]  Rafiq Ahmad,et al.  New computer vision based Snakes and Ladders algorithm for the safe trajectory of two axis CNC machines , 2012, Comput. Aided Des..

[17]  Zhengyou Zhang,et al.  Determining the Epipolar Geometry and its Uncertainty: A Review , 1998, International Journal of Computer Vision.

[18]  Mani Maran Ratnam,et al.  Measurement of nose radius wear in turning tools from a single 2D image using machine vision , 2009 .

[19]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[20]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..