Measurement and Control

doi: 10.1177/0020294015607271pmid: N/A

doi: 10.1177/0020294015607268pmid: N/A

doi: 10.1177/0020294015608025pmid: N/A

doi: 10.1177/0020294015607270pmid: N/A

Otterson, David W

doi: 10.1177/0020294015600473pmid: N/A

Wang, Wencheng; Guan, Fengnian; Ma, Shiyong; Li, Jian

doi: 10.1177/0020294015595997pmid: N/A

Fault detection is an important step in gear production, but the traditional methods are inefficient. In this paper, a novel method for gear parameter measurement is proposed based on computer vision. First, we introduce the hardware composition of a system and the main structure of the software algorithm. Next, we discuss the principles of digital image pre-processing, image segmentation, and image analysis and also analyse the detailed steps, including gear centre positioning, gear tooth root radius calculation, and the addendum circle radius calculation. Finally, we show how the other parameters of the gear can be calculated using formulas, and how possible faults can be detected accordingly. This method is simple and requires neither edge detection nor Hough transformation. Experiments show that the system is stable and fast and that it can meet the needs of gear parameter measurement, replacing manual detection in actual production.

Mahapatra, Prasant Kumar; Thareja, Rishabh; Kaur, Mandeep; Kumar, Amod

doi: 10.1177/0020294015602499pmid: N/A

This paper presents a machine vision–based precise tool positioning and verification system that may be used with milling and lathe machines, and so on. For many industrial applications, the accuracy required in machining operations is of the order of microns. The developed machine vision–based tool position verification process involves pixel calibration to compute and measure real-world minute dimensions. These measurements are based on two-dimensional spatial correlation of sequential images captured from the movement of the tool with a resolution of 250 µm. The captured sequential images are thresholded using a new bio-inspired technique named Negative Selection Algorithm, a model of Artificial Immune System. The developed system extracts the difference between the actual and target positions of the tool from the captured images through image processing and calculates the error. To compensate for the positional error, alignment commands are fed to the two-axis high precision motor. The maximum error observed was ±206 µm for 14.99999 mm movement.