Effect of Environmental Factors on the Accuracy of a Quality Inspection System Based on Transfer Learning

  • Ahmed Najah Department of Automated Manufacturing/ Al-Khwarizmi College of Engineering/ University of Baghdad
  • Faiz F. Mustafa Department of Automated Manufacturing/ Al-Khwarizmi College of Engineering/ University of Baghdad
  • Wisam S. Hacham Department of Mechatronics/ Al-Khwarizmi College of Engineering/ University of Baghdad

Abstract

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images.

So, this study aimed at testing the system performance at poor settings of lighting, background, distance and camera resolution. Experimental results implied that the system was able to show high accuracies above 90% at very bad settings and around 99% at good settings, which assures that an inspection system with good performance can be built at low costs.

 

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References

J. Wang, Y. Ma, L. Zhang, R. X. Gao and D. Wu, “Deep learning for smart manufacturing: Methods and applications,” Journal of Manufacturing Systems, vol. 48, pp. 144-156, 2018.

J. Wang, P. Fu and R. X. Gao, “Machine vision intelligence for product defect inspection based on deep learning and Hough transform,” Journal of Manufacturing Systems, vol. 51, pp. 52-60, 2019.

M. Alencastre-Miranda, R. R. Johnson and H. I. Krebs, “Convolutional Neural Networks and Transfer Learning for Quality Inspection of Different Sugarcane Varieties,” IEEE Transactions on Industrial Informatics, issue 1941-0050, pp. 1-1, 2020.

H. Würschinge, M. Mühlbauerl, M. Winter, M. Engelbrecht and N. Hanenkamp, “Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware,” Procedia CIRP, vol. 90, pp. 611-616, 2020.

Y.J. Cruz, M. Rivas, R. Quiza, G. Beruvides and R. E. Haber, “Computer vision system for welding inspection of liquefied petroleum gas pressure vessels based on combined digital image processing and deep learning techniques,” Sensors, vol. 20, issue 16, pp. 4505, 2020.

Y. Yang, L. Pan, J. Ma, R. Yang, Y. Zhu, Y. Yang and L. Zhang, “A high-performance deep learning algorithm for the automated optical inspection of laser welding,” Appl. Sci., vol. 10, issue 3, pp. 933, 2020.

Y. Yang, R. Yang, L. Pan, J. Ma, Y. Zhu, T. Diao and L. Zhang, “A lightweight deep learning algorithm for inspection of laser welding defects on safety vent of power battery,” Computers in Industry, vol. 123, issue 0166-3615, pp. 103306, 2020.

P. Cao, S. Zhang and J. Tang, “Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning,” IEEE Access, vol. 6, pp. 26241-26253, May 2018

J. Walsh, N. Mahony, S. Campbell, A. Carvalho, L. Krpalkova, G. Velasco-Hernandez, et al. “Deep learning vs. traditional computer vision,” Computer Vision Conference (CVC), Nevada, 2019, pp. 128-144.

E. S. Gadelmawla, “Computer vision algorithms for measurement and inspection of spur gears,” Measurement, vol. 44, issue 9, pp.1669-1678, July 2011.

D.K. Moru and D. Borro, “A machine vision algorithm for quality control inspection of gears,” Int J Adv Manuf Technol, vol. 106, pp. 105–123, 2020.

J. D. Kothari, “Detecting welding defects in steel plates using machine learning and computer vision algorithms,” IJAREEIE, vol. 7, issue 2278-8875, 2018.

M. Y. Sallom, “Machine vision application in manufacturing: inspection of dimensions,” The Iraqi Journal for Mechanical and Material Engineering, vol. 16, issue 3, 2016.

G. Peng, Z. Zhang and W. Li, “Computer vision algorithm for measurement and inspection of O-rings,” Measurement, vol. 94, issue 0263-2241, pp. 828-836, 2016.

C. Beltr´an-Gonz´alez, M. Bustero and A. Del Bue, “External and internal quality inspection of aerospace components,” IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy, 2020, pp. 351-355.

L. Scime and J. Beuth, “Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm,” Additive Manufacturing, vol. 19, issue 2214-8604, pp. 114-126, 2018.

E. Asoudegi and Z. Pan, “Computer vision for quality control in automated manufacturing systems,” Computers and Ind. Engng, vol. 21, issue 1-4, pp. 141-145, 1991.

P. Wang and R. X. Gao, “Transfer learning for enhanced machine fault diagnosis in manufacturing,” CIRP Annals - Manufacturing Technology, vol. 69, issue 1, pp. 413-416, 2020.

C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang and C. Liu, “A survey on deep transfer learning,” International Conference on Artificial Neural Networks – ICANN 2018, vol 11141. Springer, Cham.

S. Hou, B. Dong, H. Wang and G. Wu, “Inspection of surface defects on stay cables using a robot and transfer learning,” Automation in Construction, vol. 119, issue 0926-5805, 2020.

C.K. Shie, C.H. Chuang, C.N. Chou, M.H. Wu and E.Y. Chang, 2015, August. “Transfer representation learning for medical image analysis,” 37th Annual International Conference of the IEEE, Milan, 2015, pp. 711-714.

R. Zhang, H. Tao, L. Wu and Y. Guan, “Transfer learning with neural networks for bearing fault diagnosis in changing working conditions,” IEEE Access, vol. 5, pp.14347-14357, June 2017.

G. Bonaccorso, Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd ed. Birmingham: Packt Publishing, 2018.

N. Mahony, T. Murphy, K. Panduru, D. Riordan and J. Walsh. “Improving controller performance in a powder blending process using predictive control,” Irish Signals and Systems Conference (ISSC), Killarney, 2017, pp. 1-6.

G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger, “Densely connected convolutional networks,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, 2017, pp. 2261-2269.

J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, and L. FeiFei, “Imagenet: A large-scale hierarchical image database,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Florida, 2009, pp. 248-255.

Published
2021-06-01
How to Cite
Najah, A., Mustafa, F., & Hacham, W. (2021). Effect of Environmental Factors on the Accuracy of a Quality Inspection System Based on Transfer Learning. Al-Khwarizmi Engineering Journal, 17(2), 1-7. https://doi.org/10.22153/kej.2021.12.004