Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs
Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.
In this research, we present an adopted approach based on convolutional neural networks to design a system for quality inspection with high level of accuracy and low cost. The system is designed using transfer learning to transfer layers from a previously trained model and a fully connected neural network to classify the product’s condition into healthy or damaged. Helical gears were used as the inspected object and three cameras with differing resolutions were used to evaluate the system with colored and grayscale images. Experimental results showed high accuracy levels with colored images and even higher accuracies with grayscale images at every resolution, emphasizing the ability to build an inspection system at low costs, ease of construction and automatic extraction of image features.
M. P. Groover, “Inspection principles and practices”, in Automation, Production Systems and Computer Integrated Manufacturing, 2nd ed. M. P. Groover, Eds. Harlow: Prentice Hall, 2001, pp. 681-682.
U. Mane, A. Mahajan, E. Kargutkar and K. Dhuri, “Detection of defects in plastic gears using image processing”, IJISRT, vol. 2, issue 7, pp. 2456-2165, 2017.
Y. Qin, Y. Mao, B. Tang, Y. Wang and H. Chen, “M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems”, Mechanical Systems and Signal Processing, vol. 134, issue 0888-3270, 2019.
M. Gao, G. Yu and T. Wang, "Impulsive gear fault diagnosis using adaptive morlet wavelet filter based on alpha-stable distribution and kurtogram," in IEEE Access, vol. 7, pp. 72283-72296, 2019.
J. Cai, "Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method", Industrial Lubrication and Tribology, Vol. 71 No. 1, pp. 40-47, 2019.
T. Bettahar, C. Rahmoune, D. Benazzouz, B. Merainani, “New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric”, Advances in Mechanical Engineering, vol. 12, issue 6, 2020.
G. Yu, M. Gao and C. Jia, “A fast filtering method based on adaptive impulsive wavelet for the gear fault diagnosis”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. February 2020.
X. Liu, H. Huang and J. Xiang, “A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine”, Knowledge-Based Systems, vol. 195, issue 0950-7051, 2020.
P. Ong, T.H.C. Tieh, K. H. Lai, et al. “Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain” , J Braz. Soc. Mech. Sci. Eng., vol. 41, issue 266, 2019.
U. Urbas, D. Zorko, B. Černe, J. Tavčar and N. Vukašinović, “A method for enhanced polymer spur gear inspection based on 3D optical metrology”, Measurement, issue 0263-2241, 2020.
K. Joshi, B. Patil, “Measurement of Spur Gear Parameters Using Machine Vision,” Proceedings of International Conference on Intelligent Manufacturing and Automation. Lecture Notes in Mechanical Engineering. Springer, Singapore, 2020.
Y. Wu et al., "Detection of Gear Tooth Number and Common Normal Length Change Based on Computer Vision", 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), Dublin, Ireland, 2019, pp. 618-621.
F. I. J. Ramírez and J. M. J. Barrionuevo, "Cyber-physical system for quality control of spur gears through artificial vision techniques", 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador, 2019, pp. 1-6.
P. Kane and A. Andhare, “End of the assembly line gearbox fault inspection using artificial neural network and support vector machines”, International Journal of Acoustics and Vibrations, vol. 24, issue 1, pp. 68-84, Mar 2019.
X. Zuo, X. Lei and X. Wang, "Research on machine vision measuring method for fine-pitch gears", Proc. SPIE 11343, Ninth International Symposium on Precision Mechanical Measurements, 2019.
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.
L. Yu, X. Yao, J. Yang and C. Li, “gear fault diagnosis through vibration and acoustic signal combination based on convolutional neural network,” Information, vol. 11, issue 6, pp. 266, 2020.
K. D. Joshi, V. Chauhan and B. Surgenor, “A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach”, J Intell Manuf, vol. 31, pp. 103–125, 2020.
M. Y. Sallom, “Machine vision application in manufacturing: inspection of dimensions,” The Iraqi Journal for Mechanical and Material Engineering, vol. 16, issue 3, 2016.
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.
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.
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.
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.
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.
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.
K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biol. Cybernetics, vol. 36, pp. 193-202, Apr 1980.
I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MA: MIT press, 2016.
D. Scherer, A. Muller and S. Behnke, “Evaluation of pooling operations in convolutional architectures for object recognition”, ICANN’10 Proceedings of the 20th International Conference on Artificial Neural Networks: Part III, Greece, 2010, pp. 92–101.
M.D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” European Conference on Computer Vision, Switzerland, 2014, pp. 818-833.
A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet classification with deep convolutional neural networks”, Advances in neural information processing systems, vol. 25, issue 2, pp.1097–1105, Jan 2012.
A. Krizhevsky, “Convolutional deep belief networks on cifar-10,” May 2010.
Y.LeCun, L.D. Jackel, L. Bottou, C. Cortes, J. Denker, H. Drucker et al. “Learning algorithms for classification: A comparison on handwritten digit recognition”, Proc. 12th Int. Conf. Pattern Recognition and Neural Networks, Singapore, 1995, page 261-276.
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.
M. D. Zeiler and R. Fergus, “Stochastic pooling for regularization of deep convolutional neural networks”, 1st International Conference on Learning Representations, Scottsdale, 2013.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” 2nd International Conference on Learning Representations, Banff, 2014.
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.
A. Meiseles and L. Rokach, “Source model selection for deep learning in the time series domain”, IEEE Access, vol. 8, pp. 6190-6200, Jan 2020.
H. M. Bui, M. Lech, E. Cheng, K. Neville and I. S. Burnett, "Using grayscale images for object recognition with convolutional-recursive neural network," 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), Ha Long, 2016, pp. 321-325.
A. Najah, F. F. Mustafa and W. S. Hacham, “Effect of environmental factors on the accuracy of a quality inspection system based on transfer learning”, Submitted to Al-Khwarizmi Engineering Journal.
(Received 5 October 2020; accepted 6 December 2020)
Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.