Texture analysis for grinding wheel wear assessment using machine vision

Abstract The surface quality of the components produced in a grinding process mainly depends on the grinding wheel topography. To maintain the quality of the component, the condition of the grinding wheel should be monitored and the periodical dressing has to be done to retain the shape and the sharpness of the cutting edges. In the present paper, the machine-vision-based texture analysis methods are introduced to discriminate the surface condition of the grinding wheel. The grinding wheel images are acquired using a charge coupled device (CCD) camera at different intervals of time during the process and the texture of the grinding wheel image is analysed using appropriate statistical and fractal methods. The variation of the parameters with the condition of the grinding wheel surface is analysed and presented. The evaluated parameters are clearly discriminating the texture of the grinding wheel at every stage. The methods presented in this paper can be used to assess the grinding wheel condition effectively.

[1]  Steven Y. Liang,et al.  Machining Process Monitoring and Control: The State–of–the–Art , 2002 .

[2]  Ekkard Brinksmeier,et al.  Monitoring of grinding wheel wear , 1992 .

[3]  F. Luk,et al.  Measurement of surface roughness by a machine vision system , 1989 .

[4]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Bruce A. Wilkie,et al.  Condition monitoring of the grinding process using light scattering , 1996 .

[6]  V. Radhakrishnan,et al.  On the Possibility of Process Monitoring in Grinding by Spark Intensity Measurements , 1994 .

[7]  Bidyut Baran Chaudhuri,et al.  An efficient approach to estimate fractal dimension of textural images , 1992, Pattern Recognit..

[8]  Colin Bradley,et al.  Surface Texture Indicators of Tool Wear - A Machine Vision Approach , 2001 .

[9]  Robert M. Hodgson,et al.  Texture Measures for Carpet Wear Assessment , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  David Kerr,et al.  Assessment and visualisation of machine tool wear using computer vision , 2006 .

[11]  M. A. Younis.  On line surface roughness measurements using image processing towards an adaptive control , 1998 .

[12]  Shivakumar Gopalakrishnan,et al.  Fractal Geometry Applied to On-line Monitoring of Surface Finish , 1996 .

[13]  Stephen Malkin,et al.  Grinding Technology: Theory and Applications of Machining with Abrasives , 1989 .

[14]  Toshio Sata,et al.  Closure to “Discussion of ‘Study on Wear of Grinding Wheels: 1—Bond Fracture in Grinding Wheels’” (1963, ASME J. Eng. Ind., 85, pp. 42–43) , 1963 .

[15]  João Fernando Gomes de Oliveira,et al.  Development of an optical scanner to study wear on the working surface of grinding wheels , 1999 .

[16]  Y. S. Tarng,et al.  Surface roughness inspection by computer vision in turning operations , 2001 .

[17]  Robert Bauer,et al.  Application of region growing method to evaluate the surface condition of grinding wheels , 2004 .

[18]  Ichiro Inasaki,et al.  Sensor Fusion for Monitoring and Controlling Grinding Processes , 1999 .

[19]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[20]  Zhu Mian,et al.  Connectivity oriented fast Hough transform for tool wear monitoring , 2004, Pattern Recognit..

[21]  B. Ramamoorthy,et al.  Statistical methods to compare the texture features of machined surfaces , 1996, Pattern Recognit..

[22]  X Q Li,et al.  Intelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques , 1999 .

[23]  E. S. Gadelmawla,et al.  A vision system for surface roughness characterization using the gray level co-occurrence matrix , 2004 .

[24]  Ashraf A. Kassim,et al.  Tool wear monitoring using a fast Hough transform of images of machined surfaces , 2004, Machine Vision and Applications.

[25]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..