Skip to main content
SpringerLink
Log in

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

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

All relevant data are within the paper.

References

  1. Zhang, X.-D., Niu, H., Hou, C.-G., Marcal, A., Di, F.: An approach for tooth faults detection of planetary gearboxes based on tooth root strain signal of ring gear. Measurement (2020). https://doi.org/10.1016/j.measurement.2020.108685

    Article  Google Scholar 

  2. Ambaye, G.A., Lemu, H.G.: Dynamic analysis of spur gear with backlash using ADAMS. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.09.309

    Article  Google Scholar 

  3. Vernekar, K., Kumar, H., Gangadharan, K.V.: Gear fault detection using vibration analysis and continuous wavelet transform. Procedia Materials Science. In: ICAMME (2014)

  4. Tao, J.-G., Luo, Z., Liu, Q.: Defect detection of small modulus gear based on machine vision. In: ISPEMI (2018)

  5. Chen, G.-H., Liu, X.-L., Zhang, A.-L., Zhang, A.-J., Wang, J.-Y.: Fast measurement method of gear parameters based on convexity defect. In: International Conference on Metrology and Properties of Engineering Surfaces (2017)

  6. Fu, L., Zhang, Y.-H., Huang, Q.-L., Chen, X.-Y.: Research and application of machine vision in intelligent manufacturing. In: Chinese Control and Decision Conference (2016)

  7. Fan, Q.-Y.: Design and implementation of the detection system for the defect of components based on machine vision technique. In: International Conference on Mechanics and Control Engineering (2015)

  8. Semeniuta, O., Dransfeld, S., Martinsen, K., Falkman, P.: Towards increased intelligence and automatic improvement in industrial vision systems. Proc. CIRP (2018). https://doi.org/10.1016/j.procir.2017.12.209

    Article  Google Scholar 

  9. Song, S.-B., Liu, J.-F., Ni, H.-Y., Cao, X.-L., Pu, H., Huang, B.-X.: A new automatic thresholding algorithm for unimodal gray-level distribution images by using the gray gradient information. J. Petrol. Sci. Eng. (2018). https://doi.org/10.1016/j.petrol.2020.107074

    Article  Google Scholar 

  10. Piretzidis, D., Sideris, M.G.: Additional methods for the stable calculation of isotropic Gaussian filter coefficients: the case of a truncated filter kernel. Comput. Geosci. (2020). https://doi.org/10.1016/j.cageo.2020.104594

    Article  Google Scholar 

  11. Routray, S., Malla, P.P., Sharma, S.K., Panda, S.K., Palai, G.: A new image denoising framework using bilateral filtering based non-subsampled shearlet transform. Optik. (2020). https://doi.org/10.1016/j.ijleo.2020.164903

    Article  Google Scholar 

  12. Ichikawa, K., Kawashima, H., Shimada, M., Adachi, T., Takata, T.: A three-dimensional cross-directional bilateral filter for edge-preserving noise reduction of low-dose computed tomography images. Comput. Biol. Med. (2019). https://doi.org/10.1016/j.compbiomed.2019.103353

    Article  Google Scholar 

  13. Huang, Z.-H., Wang, Z.-C., Zhang, J., Li, Q., Shi, Y.: Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization. Optik (2021). https://doi.org/10.1016/j.ijleo.2020.165877

    Article  Google Scholar 

  14. Wu, C.-M., Cao, Z.: Entropy-like divergence based kernel fuzzy clustering for robust image segmentation. Expert Syst. Appl. (2020). https://doi.org/10.1016/j.eswa.2020.114327

    Article  Google Scholar 

  15. Gao, J.-Q., Wang, B.-B., Wang, Z.-Y., Wang, Y.-F., Kong, F.-Z.: A wavelet transform-based image segmentation method. Optik (2020). https://doi.org/10.1016/j.ijleo.2019.164123

    Article  Google Scholar 

  16. Ponti, M., Nazare, T.S., Thume, G.S.: Image quantization as a dimensionality reduction procedure in color and texture feature extraction. Neurocomputing 173(2), 385–396 (2016)

    Article  Google Scholar 

  17. Li, P., Huang, Y., Yao, K.-L.: Multi-algorithm fusion of RGB and HSV color spaces for image enhancement. In: CCC (2018)

  18. Fei, S.W.: The hybrid method of VMD–PSR–SVD and improved binary PSO–KNN for fault diagnosis of bearing. Shock Vib. (2019). https://doi.org/10.1155/2019/4954920

    Article  Google Scholar 

  19. Dong, Y.-M., Zhao, L.: Quantum behaved particle swarm optimization algorithm based on artificial fish swarm. Math. Probl. Eng. (2014). https://doi.org/10.1155/2014/592682

    Article  Google Scholar 

  20. Lv, Y.-Y., Liu, W.-J., Wang, Z., Zhang, Z.-H.: WSN location technology based on hybrid GA–PSO–BP algorithm for indoor three-dimensional space. Wirel. Pers. Commun. (2020). https://doi.org/10.1007/s11277-020-07357-4

    Article  Google Scholar 

  21. Shen, C.-Q., Qi, Y.-M., Wang, J., Cai, G.-G., Zhu, Z.-K.: An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder. Eng. Appl. Artif. Intell. (2018). https://doi.org/10.1016/j.engappai.2018.09.010

    Article  Google Scholar 

  22. Mousavi, M., Yap, H.J., Musa, S.-N., Dawal, S.Z.M.: A fuzzy hybrid GA–PSO algorithm for multi-objective AGV scheduling in FMS. Int. J. Simul. Model. 16(1), 58–71 (2017)

    Article  Google Scholar 

  23. Li, W., Wang, X.-M., Jiang, D.-N., Li, Y.-J., Liang, C.-L.: Prediction of octane number of hydrogenated gasoline components in finished gasoline blending based on SHPSO–GA–BP. J. Chem. Ind. 71(07), 3191–3200 (2020)

    Google Scholar 

  24. Chen, L.-P., Liu, J., Ha, W.-T.: Cloud service security evaluation of smart grid using deep belief network. Int. J. Sens. Netw. 33(2), 109–121 (2020)

    Article  Google Scholar 

  25. Nazir, H.M., Hussain, I., Faisal, M., Shoukry, A.M., Gani, S., Ahmad, I.: Development of multidecomposition hybrid model for hydrological time series analysis. Complexity (2019). https://doi.org/10.1155/2019/2782715

    Article  Google Scholar 

Download references

Acknowledgements

The research is funded partially by the Agricultural Science and Technology Independent Innovation Fund of Jiangsu Province (CX(19)3081), and the Key Research and Development Program of Jiangsu Province (BE2018127, BE2020317).

Author information

Authors and Affiliations

  1. College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China

    Maohua Xiao, Weichen Wang, Xiaojie Shen & Yue Zhu

  2. Faculty of Agriculture, University of South Bohemia, 1668, Studentska, Czech Republic

    Petr Bartos

  3. College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Ürümqi, 830052, China

    Yilidaer Yiliyasi

Authors
  1. Maohua Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weichen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaojie Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Petr Bartos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yilidaer Yiliyasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maohua Xiao.

Ethics declarations

Conflict of interest

The authors declare that there are no competing interests regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, M., Wang, W., Shen, X. et al. Research on defect detection method of powder metallurgy gear based on machine vision. Machine Vision and Applications 32, 51 (2021). https://doi.org/10.1007/s00138-021-01177-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-021-01177-7

Keywords

Search

Navigation