Research on defect detection method of powder metallurgy gear based on machine vision
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12220%2F21%3A43903135" target="_blank" >RIV/60076658:12220/21:43903135 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00138-021-01177-7" target="_blank" >https://link.springer.com/article/10.1007/s00138-021-01177-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00138-021-01177-7" target="_blank" >10.1007/s00138-021-01177-7</a>
Alternative languages
Result language
angličtina
Original language name
Research on defect detection method of powder metallurgy gear based on machine vision
Original language description
Powder metallurgy gears are often accompanied by broken teeth, abrasion, scratches and crack defects. In order to eliminate the defective gears in gear production and improve the yield of gears, this paper presents an improved GA-PSO algorithm, called the SHGA-PSO algorithm. Firstly, the gear images were preprocessed by bilateral filtering, and the images were segmented by the Sobel operator. Then, the geometrical shape, texture feature and color features of the sample were extracted. Next, the BP neural network was reconstructed and SHGA-PSO algorithm was used optimize its structure and weights. Finally, four different gear defect samples were brought into the neural network for calculation, and the performance of the SHGA-PSO algorithm was compared with the GA, PSO and GA-PSO algorithms. Compared with GA-BP algorithm, PSO-BP algorithm, and GA-PSO-BP algorithm, the defect diagnosis of SHGA-PSO-BP algorithm not only enhanced generalization ability, but also improved recognition accuracy.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20501 - Materials engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Vision and Applications
ISSN
0932-8092
e-ISSN
—
Volume of the periodical
32
Issue of the periodical within the volume
51
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
1-13
UT code for WoS article
000622733900001
EID of the result in the Scopus database
—