Steel Ball Defect Detection System Using Automatic Vertical Rotating Mechanism and Convolutional Neural Network
Source abstract
Abstract
Precision steel balls are critical components in precision bearings. Surface defects on the steel balls will significantly reduce their useful life and cause linear or rotational transmission errors. Human visual inspection o... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
Show source-provided references
1. A survey of non-destructive techniques used for inspection of bearing steel balls10.1016/j.measurement.2020.107773 ↗
2. Optical inspection of ball bearing defects10.1088/0957-0233/18/9/N01 ↗
3. Detection and recognition of steel ball surface defect based on MATLAB10.4028/www.scientific.net/KEM.416.603 ↗
4. Design of steel ball surface quality detection system based on machine vision
5. Development of an automatic inspection system for surface defects of precision steel balls
6. A real-time surface inspection system for precision steel balls based on machine vision10.1088/0957-0233/27/7/074010 ↗
7. Research on steel ball surface defect detection device based on machine vision
8. Application of a saddle-type eddy current sensor in steel ball surface-defect inspection10.3390/s17122814 ↗
9. A steel ball surface quality inspection method based on a circumferential eddy current array sensor10.3390/s17071536 ↗
10. A method of steel ball surface quality inspection based on flexible arrayed eddy current sensor10.1016/j.measurement.2019.05.056 ↗
11. Research on dual wavelength coaxial optical fiber sensor for detecting steel ball surface defects10.1016/j.measurement.2018.10.026 ↗
12. Detection of surface defects on steel ball bearings in production process using a capacitive sensor10.1016/0263-2241(96)00007-3 ↗
13. Improved illumination for vision-based defect inspection of highly reflective metal surface10.3788/COL201311.021102 ↗
14. Vision-based surface defect inspection of metal balls10.1088/0957-0233/22/10/107001 ↗
15. Gonzalez · Digital image processing · 2018
16. Comparative analysis of color-and grayscale-based feature descriptions for image recognition10.1134/S1054661813030115 ↗
17. Color to grayscale conversion based on neighborhood pixels effect approach for digital image
18. A comparative study of histogram equalization-based image enhancement techniques for brightness preservation and contrast enhancement
19. Using weighted dynamic range for histogram equalization to improve the image contrast10.1186/1687-5281-2014-44 ↗
20. Image enhancement using local and global histogram equalization technique and their comparison
21. Defect detection in printed circuit boards with pre-trained feature extraction methodology with convolution neural networks
22. Liu B, Chen D, Qi X. YOLO-pdd: a novel multi-scale PCB defect detection method using deep representations with sequential images. arXiv:2407.15427. 2024.10.1007/978-981-96-6596-9_21 ↗
23. Customized convolutional neural networks technology for machined product inspection10.3390/app12063014 ↗
24. Feature extraction and classification of photovoltaic panels based on convolutional neural network
25. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:149.1556. 2015.
26. An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment10.1016/j.ymssp.2024.111596 ↗
27. Redmon J, Farhadi A, YOLO9000: better, faster, stronger. arXiv:1612.08242. 2016.10.1109/CVPR.2017.690 ↗