Abstract
Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.
NTU-PU Collaborative Research Grant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ashour, M. W., Halin, A. A., Khalid, F., Abdullah, L. N., & Darwish, S. H. (2015). Texture-based classification of workpiece surface images using the support vector machine. International Journal of Software Engineering and Its Applications, 9(10), 147–160.
Bhandari, S. H., Deshpande, S., & Deshpande, S. (2008). A simple approach to surface defect detection. 2008 IEEE Region, 10, 8–10.
Caleb, P., & Steuer, M. (2000). Classification of surface defects on hot rolled steel using adaptive learning methods. In KES’2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No. 00TH8516) (Vol. 1, pp. 103–108). IEEE.
Chen, J., Liu, Z., Wang, H., Núñez, A., & Han, Z. (2017). Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 67(2), 257–269.
Elangovan, S. B. T. K. R. (2019). Fabrication and analysis of polymer bolt and nut assembly by additive manufacturing system.
Gai, X., Ye, P., Wang, J., & Wang, B. (2020). Research on defect detection method for steel metal surface based on deep learning. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 637–641). IEEE.
Giben, X., Patel, V. M., & Chellappa, R. (2015). Material classification and semantic segmentation of railway track images with deep convolutional neural networks. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 621–625). IEEE.
Kou, X., Liu, S., Cheng, K., & Qian, Y. (2021). Development of a yolo-v3-based model for detecting defects on steel strip surface. Measurement, 182, 109454.
Liu, W., Liu, Z., Nunez, A., & Han, Z. (2020). Unified deep learning architecture for the detection of all catenary support components. IEEE Access, 8, 17049–17059.
Montgomery, D. C. (2017). Design and analysis of experiments. Wiley.
Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014). Review of vision-based steel surface inspection systems. EURASIP Journal on Image and Video Processing, 2014(1), 1–19.
Oehlert, G. W. (2010). A first course in design and analysis of experiments.
Park, J. K., Kwon, B. K., Park, J. H., & Kang, D. J. (2016). Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(3), 303–310.
Saiz, F. A., Serrano, I., Barandiarán, I., & Sánchez, J. R. (2018). A robust and fast deep learning-based method for defect classification in steel surfaces. In 2018 International Conference on Intelligent Systems (IS), (pp. 455–460). IEEE.
Shaheen, M. A., Foster, A. S., Cunningham, L. S., & Afshan, S. (2020). Behaviour of stainless and high strength steel bolt assemblies at elevated temperatures-a review. Fire Safety Journal, 113, 102975.
Sharifzadeh, M., Amirfattahi, R., Sadri, S., Alirezaee, S., & Ahmadi, M. (2008). Detection of steel defect using the image processing algorithms. In The International Conference on Electrical Engineering (pp. 1–7). Military Technical College.
Song, L., Li, X., Yang, Y., Zhu, X., Guo, Q., & Yang, H. (2018). Detection of micro-defects on metal screw surfaces based on deep convolutional neural networks. Sensors, 18(11), 3709.
Taheritanjani, S., Schoenfeld, R., & Bruegge, B. (2019). Automatic damage detection of fasteners in overhaul processes. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) (pp. 1289–1295), IEEE.
Xue, B., & Wu, Z. (2021). Key technologies of steel plate surface defect detection system based on artificial intelligence machine vision. Wireless Communications and Mobile Computing, 2021.
Xue, P., Jiang, C., & Pang, H. (2021). Detection of various types of metal surface defects based on image processing. Traitement du Signal, 38(4).
Yun, J. P., Choi, S., Kim, J. W., & Kim, S. W. (2009). Automatic detection of cracks in raw steel block using gabor filter optimized by univariate dynamic encoding algorithm for searches (UDEAS). NDT and E International, 42(5), 389–397. https://doi.org/10.1016/j.ndteint.2009.01.007
Zhao, Z., Qi, H., Qi, Y., Zhang, K., Zhai, Y., & Zhao, W. (2020). Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines. IEEE Transactions on Instrumentation and Measurement, 69(9), 6080–6091.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, M. et al. (2023). Taguchi Based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-5443-6_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5442-9
Online ISBN: 978-981-19-5443-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)