A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm × 0.4 mm and the precision of dimension measurement is about 40–50 μm. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production.

[1]  Vincent Frémont,et al.  Vision-Based People Detection System for Heavy Machine Applications , 2016, Sensors.

[2]  Guo Fu Yin,et al.  Surface Defect Inspection and Classification of Segment Magnet by Using Machine Vision Technique , 2011 .

[3]  Du-Ming Tsai,et al.  Defect detection of uneven brightness in low-contrast images using basis image representation , 2010, Pattern Recognit..

[4]  Jianping Wu,et al.  Real-time Robust Algorithm for Circle Object Detection , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[5]  Pascual Campoy Cervera,et al.  Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection , 2015, Sensors.

[6]  Zhen Liu,et al.  Fast and Flexible Movable Vision Measurement for the Surface of a Large-Sized Object , 2015, Sensors.

[7]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification , 2009, Expert Syst. Appl..

[8]  Anand Parey,et al.  Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size , 2007 .

[9]  Antônio Otávio Fernandes,et al.  Machine vision applications and development aspects , 2011, 2011 9th IEEE International Conference on Control and Automation (ICCA).

[10]  Te-Hsiu Sun,et al.  Electric contacts inspection using machine vision , 2010, Image Vis. Comput..

[11]  Yuting Ye,et al.  A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle , 2015, Sensors.

[12]  Lin Liu,et al.  Method of circle detection in PCB optics image based on improved point Hough transform , 2009, International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT).

[13]  William D. Mark,et al.  A simple frequency-domain algorithm for early detection of damaged gear teeth , 2010 .

[14]  José Blasco,et al.  Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm , 2007 .

[15]  Daniel F. García,et al.  Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review , 2016, Sensors.

[16]  J. Gómez-Sanchís,et al.  Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[17]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[18]  Zhang Mingzhu,et al.  A new method of circle’s center and radius detection in image processing , 2008, 2008 IEEE International Conference on Automation and Logistics.

[19]  Andrew Ball,et al.  Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network , 2011 .