Paper
19 November 2021 On line identification method of forming defects of small module plastic gears
Zhaoyao Shi, Yiming Fang, Huixu Song
Author Affiliations +
Proceedings Volume 12059, Tenth International Symposium on Precision Mechanical Measurements; 120590Q (2021) https://doi.org/10.1117/12.2611910
Event: Tenth International Symposium on Precision Mechanical Measurements, 2021, Qingdao, China
Abstract
Due to its anisotropic shrinkage characteristics and the instability of forming process, small module plastic gears are easy to produce complex appearance defects after forming. The traditional manual detection can not accurately identify all kinds of defects, let alone complete the full inspection and statistical analysis. Therefore, a prototype of small module plastic gear on-line detection and sorting system based on machine vision is developed. The system has the function of on-line classification and identification of plastic gear surface defects, and can realize full inspection. The watershed algorithm based on local extreme value and Canny edge extraction algorithm can accurately extract the gear defect area, mark the defect location and count the defect characteristics, complete the determination of defects such as size out of tolerance, shrinkage, burr, surface foreign matter and incompletely filled part in 0.3s, and display the defect location through the human-computer interaction interface, with a discrimination accuracy of 95%.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhaoyao Shi, Yiming Fang, and Huixu Song "On line identification method of forming defects of small module plastic gears", Proc. SPIE 12059, Tenth International Symposium on Precision Mechanical Measurements, 120590Q (19 November 2021); https://doi.org/10.1117/12.2611910
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Teeth

Prototyping

Inspection

Tolerancing

Image processing

Machine vision

Detection and tracking algorithms

Back to Top