Research on the Method of Identifying the Collapsed Angle Surface of Fine Punching Small Gear

Authors

  • Lingzhi Tang
  • Yitao Chen
  • Zhifeng Song
  • Zhen Cai

DOI:

https://doi.org/10.54097/e0w46a52

Keywords:

Fine punching gear; Collapsed angle; Image identification; Halcon; Feature.

Abstract

Aiming at the requirement of automatic assembly of automobile rear wiper for automatic identification of the collapsed corner surface of finely-punched pinion, this paper proposes a method based on the mean value of the area of multi-toothed region of finely-punched pinion as the characteristic parameter, which can better identify the collapsed corner surface. Firstly, the industrial camera is used to obtain the pinion image, and the Halcon operator is used to fit the circle in the pre-segmented region to obtain the pinion circle position parameter, and the parameter is used as the reference to accurately segment the toothed annulus ROI, and then carry out the dynamic thresholding of the region, merging the regions, shape convex package transformation, and the conditional filtering to obtain the multiple toothed regions, and then rank the toothed regions, and the maximum top three toothed regions area mean value is the feature parameter to achieve the fine blanking pinion surface automatic identification of the collapsed corner surface. The mean value of the area of the first three tooth-shaped areas is used as the characteristic parameter to achieve the identification of the collapsed angle surface of the fine blanking pinion gear. The application on the assembly line shows that the recognition accuracy of this method reaches 99.9%, with good robustness and stable operation, which meets the production requirements, and also provides a method for the recognition of collapsed corner surfaces of other fine blanking parts.

Downloads

Download data is not yet available.

References

ZhouDingHe,SongZhiFeng,LinfuSheng. A machine vision-based method for detecting painting defects on automotive parts[J]. Plating and Finishing,2021,40(16):1292-1300

Wang Dongsheng. Rapid inspection system for tooth shape of rotary tooth parts [D]. Hubei:Wuhan University of Technology, 2016.

Priyanka Khandelwal,Pankaj Kumar Gautam. Mechanical Part Surface Defect Detection using Crack Extraction Approach[J]. IJCA.2014,8 (20):13-17.

Yuan S. C. Research on key technology of visual inspection data processing for product surfacedefects [D]. Jiangxi:Nanchang University,2015.

Sohail Akhtar,Adarsh Tandiya,Medhat Moussa,et al. A Robotics Inspection System for Detecting Defects on Semi-specular Painted Automotive Surfaces[J]. Imaging Syst Technol. 2020,5 (7):106-115.

Tatsuya Yamazaki ,undefined undefined, Akito Fukui,et al. Defect Detection for Forged Metal Parts by Image Processing [J]. IJFCC.2020,3 (9):23-26.

Jyotismita Chaki,Nilanjan Dey. A Beginner's Guideto Image Preprocessing Techniques [J]. IEEE International Conference on Information and Automation.2018,10 (6):66-68.

Adarsh Tandiya, Sohail Akthar, Medhat Moussa,et al.Automotive Semi-specular Surface Defect Detection System [J]. ICFSP.2018,5 (47):112-115.

M Dhivya,M Renuka Devi. Detection of Structural Defects in Fabric Parts Using a Novel Edge Detection Method[J]. IEEE International Conference on Smart City.2018,12 (62):1036-1043.

BLAYVAS I, BRUCKSTEIN A, KIMMEL R. Efficient computation of adaptive threshold surfaces for image binarization[J]. Pattern Recognition: The Journal of the Pattern Recognition Society,2006,39(1):89-101.

Carsten Steger, Markus Ulrich, Christian Wiedemann. Machine Vision Algorithms and Applications [M] et al. Translation, Tsinghua University Press, Beijing, 2019.

HIGH TIME, RENKE, GUO Yongcai. Crack defect detection technology based on machine vision[J]. Aerospace Precision Manufacturing Technology,2007,43(5):23-25.

Murtha AL-Yoonus,Aqeel Adel Yaseen. Deformation Detection and Classification system for Car parts Products Using Image Processing Algorithms[J]. IOP Conf. Ser.: Mater. Sci. Eng.2019,5 (518):42-46.

Mohammed Benmoussat Klaus Spinner, Mireille Guillaume. Surface defect detection of metal parts: Use of multimodal illuminations and hyperspectral imaging algorithms [J]. IEEC.2012,7 (36):563-582.

B. Du. Machine vision:description and implementation using HALCON [M]. Tsinghua University Press, 2021.8.

Downloads

Published

14 May 2024

Issue

Section

Articles

How to Cite

Tang, L., Chen, Y., Song, Z., & Cai, Z. (2024). Research on the Method of Identifying the Collapsed Angle Surface of Fine Punching Small Gear. International Journal of Education and Humanities, 14(1), 253-259. https://doi.org/10.54097/e0w46a52