The automatic image inspection system for measuring dimensional parameters of a saw blade

Author(s):  
Yung-Cheng Wang ◽  
Jui-Chang Lin ◽  
Shih-Fong Chiu
1995 ◽  
Vol 115 (3) ◽  
pp. 452-459
Author(s):  
Saburo Okada ◽  
Masaaki Imade ◽  
Hidekazu Miyauchi ◽  
Tetsuhiro Sumimoto ◽  
Hideki Yamamoto

2017 ◽  
Vol 872 ◽  
pp. 378-382
Author(s):  
Xin Qiang Ma ◽  
Hai Long Ge ◽  
Jian Ma ◽  
Wei Cheng ◽  
Yuan Ren

Aiming at the problem of the complicated process, the low precision and efficiency of crack detection and key geometric feature of the circular saw blade, the quality detection system of the circular saw blade based on the nondestructive testing and laser measurement technology is studied. By using the laser measurement technology and optimizing the data processing method, the face runout, the thickness of the circular saw blade are measured. The crack defects in the tooth root region of the circular saw blade are detected by eddy current nondestructive testing technology. The system achieves the radial detection of 1mm radial sawing, the detection accuracy of the round beating is> 0.03mm, and the thickness measurement accuracy is> 0.005mm.


2021 ◽  
Author(s):  
Yi Zhu

Automated industrial image inspection system has attracted a great deal of interest in recent years. In this thesis, a new method is presented by combining a statistics method with a neural networks method, which could reduce the interference of machine dynamics and improve the inspection accuracy. Different from the pixel-based or feature-based methods, the proposed method is based on two indices of an image, which are the variances of the rows and columns of the image. For image inspection, first neural networks are trained using these two indices from a set of good images in order to establish a tolerance zone. Then, the two indices of each inspection image are computed through trained neural networks and compared with the tolerance zone. A defective item is detected if either index falls out of the tolerance zone. The other contributions, such as two-point based image registration method and defect simulation algorithms, also help to improve the performance of inspection. Experimental results demonstrate that the proposed approach has a better performance in comparison with traditional statistics approach.


2015 ◽  
Vol 81 (12) ◽  
pp. 1193-1197 ◽  
Author(s):  
Yuichiro YOSHIMURA ◽  
Yudai FURUYA ◽  
Shuta NEGORO ◽  
Kimiya AOKI ◽  
Seiji YAMATOGI ◽  
...  

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