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

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.