SpringerLink
Log in

Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Inconsistent edges of mechanical workpieces in the same batch are one of the main reasons that lead to different machining performance. A full recognition of the inconsistent features has significant impact on enhancement of their intelligent machining ability. An intelligent hybrid strategy is proposed for edge inconsistent feature detection by machine vision, in which deep learning is combined with variable geometric model together to conduct the function. A deep convolutional neural network based feature classification model is established with K-Means clustering tactic. Supported on the classification model, a variable geometric model for specific edge inconsistency is given with an inconsistency evaluation function to investigate the match degree between the geometric model and the actual detected edge, and then particle swarm optimization algorithm is applied to find the solution of this geometric model. Detection experiments are carried out on a domestic servo-driven vision measuring platform to verify the performance of the proposed approach. The results show that the combined scheme can classify the different type of geometric contour of edge features with 100% correctness, and better evaluation performance by dice similarity index and Hausdorff distance in comparisons with other recent candidate methodologies. It is also indicated that the presented method provides a good recognition of the geometrical shape with less than 0.06mm maximum error for workpiece with 142 × 119mm size in the visual field.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Qiu H, Li Y, Li Y, new method A (2001) device for motion accuracy measurement of NC machine tools part 2: device error identification and trajectory measurement of general planar motions. Int J Mach Tools Manuf 41(4):535–554

    Article  Google Scholar 

  2. Zhang Y, Lefebvre D, Li QL (2017) Automatic detection of defects in tire radiographic images. IEEE Trans Autom Sci Eng 14(3):1378–1386

    Article  Google Scholar 

  3. Thongkamwitoon T, Muammar H, Dragotti PL (2015) An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Trans Inform Forens Secur 10(5):953– 968

    Article  Google Scholar 

  4. Chong Y, Song YH, Zhang YL (2016) Scene text localization using edge analysis and feature pool. Neurocomputing 175:652–661

    Article  Google Scholar 

  5. Chen TJ, Wang Y, **ao CY, Wu QMJ (2016) A machine vision apparatus and method for can-end inspection. IEEE Trans Instrum Measur 65(9):2055–2066

    Article  Google Scholar 

  6. Liu HW, Yin JP, Luo XD, Zhang SC (2018) Foreword to the special issue on recent advances on pattern recognition and artificial intelligence. Neural Comput Appl 29(1):1–2

    Article  Google Scholar 

  7. Fan J, Wang J (2018) A two-phase fuzzy clustering algorithm based on neurodynamic optimization with its application for PolSAR image segmentation. IEEE Trans Fuzzy Syst 26(1):72–83

    Article  Google Scholar 

  8. Kusakunniran W, Wu Q, Ritthipravat P (2018) Hard exudates segmentation based on learned initial seeds and iterative graph cut. Comput Methods Programs Biomed 158:173–183

    Article  Google Scholar 

  9. Tsai DM, Hsieh YC (2017) Machine vision-based positioning and inspection using expectation-maximization technique. IEEE Trans Instrum Meas 66(11):2858–2868

    Article  Google Scholar 

  10. Han Y, Wu YB, Cao DH (2017) Defect detection on button surfaces with the weighted least-squares model. Front Optoelectron 10(2):151–159

    Article  Google Scholar 

  11. Michal S, Bartosz P, Marcin M (2016) Machine vision micro-milling tool wear inspection by image reconstruction and light Ref12ectance. Precis Eng 44:236–244

    Article  Google Scholar 

  12. Guo G, Wang Y, Jiang TT (2014) A shape reconstructability measure of object part importance with applications to object detection and localization. Int J Comput Vis 108(3):241–258

    Article  MathSciNet  MATH  Google Scholar 

  13. Shahabi HH, Ratnam MM (2010) Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach. Int J Adv Manuf Technol 48(1-4):1213–1226

    Article  Google Scholar 

  14. Cano T, Chapelle F, Lavest JM, Ray P (2008) A new approach to identifying the elastic behaviour of a manufacturing machine. Int J Mach Tools Manuf 48(14):1569–1577

    Article  Google Scholar 

  15. Koroglu MT, Passino KM (2014) Illumination balancing algorithm for smart lights. IEEE Trans Control Syst Technol 22(2):557–567

    Article  Google Scholar 

  16. Wang JJ, Ma YL, Zhang L, Gao RX, Wu DZ (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156

    Article  Google Scholar 

  17. Fujita H, Cimr D (2019) Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Appl Intell 49:3383–3391. https://doi.org/10.1007/s10489-019-01461-0

    Article  Google Scholar 

  18. Zhou F, Yang S, Fujita H, et al. Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2019.07.008

  19. Park JK, Kwon BK, Park JH, Kang DJ (2016) Machine learning-based imaging system for surface defect inspection. Int J Precis Eng Manuf-Green Technol 3(3):303–310

    Article  Google Scholar 

  20. Zhou SF, Shen W, Zeng D (2016) Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Process: Image Commun 47:358–368

    Google Scholar 

  21. Hu ZL, Tang JS, Wang ZM, Zhang K, Zhang L (2018) Deep learning for image-based cancer detection and diagnosis a survey. Pattern Recogn 83:134–149

    Article  Google Scholar 

  22. Wen CB, Liu PL, Ma WB (2018) Edge detection with feature re-extraction deep convolutional neural network. J Vis Commun Image Represent 57:84–90

    Article  Google Scholar 

  23. Diao WH, **an S, Dou FZ, Yan ML, Wang H, Fu K (2015) Object recognition in remote sensing images using sparse deep belief networks. Remote Sensing Lett 6(10):745–754

    Article  Google Scholar 

  24. Chowdhury A, Kautz E, Yener B (2016) Image driven machine learning methods for microstructure recognition. Comput Mater Sci 123:176–187

    Article  Google Scholar 

  25. Liu L, Fieguth P, Guo Y (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn 62:135–160

    Article  Google Scholar 

  26. Sarkar S, Venugopalan V, Reddy K (2017) Deep learning for automated occlusion edge detection in RGB-D frames study. J Signal Process Syst Signal Image Video Technol 88:205–217

    Article  Google Scholar 

  27. Zhang XS, Gao T, Gao DD (2018) A new deep spatial transformer convolutional neural network for image saliency detection. Des Autom Embed Syst 22(3):243–256

    Article  Google Scholar 

  28. Hoang ND, Nguyen QL, Tran VD (2018) Automatic recognition of asphalt pavement cracks using meta heuristic optimized edge detection algorithms and convolution neural network. Autom Constr 94:203–213

    Article  Google Scholar 

  29. Zhu ZX, Rao C, Bai S, Latecki LJ (2019) Training convolutional neural network from multi-domain contour images for 3D shape retrieval. Pattern Recogn Lett 119:41–48

    Article  Google Scholar 

  30. Mao YH, Shen JJ, Gui XL (2018) A study on deep belief net for branch prediction. IEEE Access 6 (99):10779–10786

    Article  Google Scholar 

  31. Gu JX, Wang ZH, Jason K, Ma LY, Amir S, Bing S, Liu T, Wang XX, Wang G (2015) Recent advances in convolutional neural networks. Comput Sci 77:354–377

    Google Scholar 

  32. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  33. Bengio Y (2009) Deep learning architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MATH  Google Scholar 

  34. Chen LH, Xu ZS, Wang H, Liu SS (2016) An ordered clustering algorithm based on K-means and the PROMETHEE method. Int J Mach Learn Cybern 9(6):1–10

    Google Scholar 

  35. Hu YC, Huan C, Nian FD, Wang Y, Li T (2016) Dense crowd counting from still images with convolutional neural networks. J Vis Commun Image Represent 38:530–539

    Article  Google Scholar 

  36. Zhou W, **e JH, Li GP, Yuan YS (2017) High-precision estimation of target range, radial velocity, and azimuth in mechanical scanning LFMCW radar. IET Radar Sonar Navigation 11(11):1664–1672

    Article  Google Scholar 

  37. Li HL, Meng FM, Luo B, Zhu SY (2014) Repairing bad co-segmentation using its quality evaluation and segment propagation. IEEE Trans Image Process Publ IEEE Signal Process Soc 23(8):3545–3559

    MathSciNet  MATH  Google Scholar 

  38. Li S, Cheng C (2017) Particle swarm optimization with fitness adjustment parameters. Comput Industr Eng 113:831–841

    Article  Google Scholar 

  39. Kuo YL, Tai CW (2015) A simple and efficient median filter for removing high-density impulse noise in images. Int J Fuzzy Syst 17(1):67–75

    Article  Google Scholar 

  40. Gao YY, Hu HM, Li B (2018) Naturalness preserved nonuniform illumination estimation for image enhancement based on retinex. IEEE Trans Multimed 20(2):335–344

    Article  Google Scholar 

  41. Khambampati AK, Liu D, Konki SK, Kim KY (2018) An automatic detection of the ROI using Otsu thresholding in nonlinear difference EIT imaging. IEEE Sensors J 18(12):5133–5142

    Article  Google Scholar 

  42. Suresh S, Lal S (2017) Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl Soft Comput 61:622–641

    Article  Google Scholar 

  43. Wang F, Liu WY, Liu HJ, Cheng J (2018) Additive margin softmax for face verification. IEEE Signal Process Lett 25(7):926–930

    Article  Google Scholar 

  44. Sudan J, Son LH, Raghvendra K, Ishaani P, Florentin S, Long HV (2019) Neutrosophic image segmentation with dice coefficients. Measurement 134:762–772

    Article  Google Scholar 

  45. Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 15:850–863

    Article  Google Scholar 

  46. Rong D, Rao XQ, Ying YB (2017) Computer vision detection of surface defeat on oranges by means of a sliding comparison window local segmentation algorithm. Comput Electron Agric 137:59–68

    Article  Google Scholar 

  47. Lin HF, Li J, Zhou PY, Liang DC, Li DM (2017) Saliency detection using adaptive background template. IET Comput Vis 11(6):389–397

    Article  Google Scholar 

  48. Zhu WB, Luo ZX, Lim A, Oon WC (2016) A fast implementation for the 2D/3D box placement problem. Comput Optim Appl 63(2):585–612

    Article  MathSciNet  MATH  Google Scholar 

  49. **g JF, Chen S, Li PF (2016) Fabric defect detection based on golden image subtraction. Color Technol 133:26–39

    Article  Google Scholar 

  50. Garrido L, Guerrieri M, Igual L (2015) Image segmentation with cage active contours. IEEE Trans Image Process 24(12):5557–5566

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by National Natural Science Foundation of China [grant number 51005158]; and “High-end Numerical Control Machine and Basic Manufacturing Equipment” Key Sic-tech Special Project [grant number 2011ZX04003-031].

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    **ankun Lin, **n Wang & Li Li

Authors
  1. **ankun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. **n Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **ankun Lin.

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

Lin, X., Wang, X. & Li, L. Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model. Appl Intell 50, 2105–2119 (2020). https://doi.org/10.1007/s10489-020-01641-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01641-3

Keywords

Search

Navigation