Research on defect detection method of powder metallurgy gear based on machine vision

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Research on defect detection method of powder metallurgy gear based on machine vision

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Research on defect detection method of powder metallurgy gear based on machine vision

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Machine Vision and Applications

ISSN

0932-8092

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

51

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000622733900001

EID výsledku v databázi Scopus

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