Abstract
Manufacturing has experienced tremendous changes from industry 1.0 to industry 4.0 with the advancement of technology in fast-developing areas such as computing, image processing, automation, machine vision, machine learning along with big data and Internet of things. Machine tools in industry 4.0 shall have the ability to identify materials which they handle so that they can make and implement certain decisions on their own as needed. This paper aims to present a generalized methodology for automated material identification using machine vision and machine learning technologies to contribute to the cognitive abilities of machine tools as wells as material handling devices such as robots deployed in industry 4.0. A dataset of the surfaces of four materials (Aluminium, Copper, Medium density fibre board, and Mild steel) that need to be identified and classified is prepared and processed to extract red, green and blue color components of RGB color model. These color components are used as features while training the machine learning algorithm. Support vector machine is used as a classifier and other classification algorithms such as Decision trees, Random forests, Logistic regression, and k-Nearest Neighbor are also applied to the prepared data set. The capability of the proposed methodology to identify the different group of materials is verified with the images available in an open source database. The methodology presented has been validated by conducting four experiments for checking the classification accuracies of the classifier. Its robustness has also been checked for various camera orientations, illumination levels, and focal length of the lens. The results presented show that the proposed scheme can be implemented in an existing manufacturing setup without major modifications.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology,2(3), 27:1–27:27.
Demir, H. (2018). Classification of texture images based on the histogram of oriented gradients using support vector machines. Istanbul University Journal of Electrical & Electronics Engineering,18(1), 90–94. https://doi.org/10.5152/iujeee.2018.1814.
Denkena, B., Bergmann, B., & Witt, M. (2018). Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1404-0.
Dowe, D. L., & Hern, M. V. (2012). Measuring cognitive abilities of machines, humans and non-human animals in a unified way : Towards Universal Psychometrics. Technical Report 2012/267, Faculty of Information Technology, Clayton School of I.T., Monash University, Australia, March 2012.
Fritz, M., Hayman, E., Caputo, B., & Eklundh, J.-O. (2004). THE KTH-TIPS database. Retrieved March 26, 2019 from http://www.nada.kth.se/cvap/databases/kth-tips/download.html.
Hien, T. N., Street, G., & Ward, L. T. (2017). Autonomous machining systems. Vietnam Journal of Science and Technology,55(3), 368–381. https://doi.org/10.15625/2525-2518/55/3/8632.
Joshi, K. D., Chauhan, V., & Surgenor, B. (2018). A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1438-3.
Karen, S., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9.
Kita, Y., Ishikawa, H., & Masuda, T. (2017). Guest editorial: Machine vision applications. International Journal of Computer Vision,122(2), 191–192. https://doi.org/10.1007/s11263-017-0990-1.
Kucukoglu, I., Atici-Ulusu, H., Gunduz, T., & Tokcalar, O. (2018). Application of the artificial neural network method to detect defective assembling processes by using a wearable technology. Journal of Manufacturing Systems,49(June), 163–171. https://doi.org/10.1016/j.jmsy.2018.10.001.
Kuo, C.-F. J., Huang, Y.-J., Su, T.-L., & Shih, C.-Y. (2008). Computerized color distinguishing system for color printed fabric by using the approach of probabilistic neural network. Polymer-Plastics Technology and Engineering,47(3), 264–272. https://doi.org/10.1080/03602550701866808.
Kwon, O., Kim, H. G., Ham, M. J., Kim, W., Kim, G. H., Cho, J. H., et al. (2018). A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1451-6.
Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2018). Automated defect inspection of LED chip using deep convolutional neural network. Journal of Intelligent Manufacturing,2014, 1–10. https://doi.org/10.1007/s10845-018-1415-x.
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: Characteristics, technologies and enabling factors. Journal of Engineering Manufacture,233(5), 1342–1361. https://doi.org/10.1177/0954405417736547.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research,56(3), 1118–1136. https://doi.org/10.1080/00207543.2017.1372647.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research,12, 2825–2830.
Pham, D. T., & Afify, A. A. (2005). Machine-learning techniques and their applications in manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture,219(5), 395–412. https://doi.org/10.1243/095440505X32274.
Pimenov, D. Y., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing,29(5), 1045–1061. https://doi.org/10.1007/s10845-017-1381-8.
Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. Journal of Open Source Software,3(24), 638. https://doi.org/10.21105/joss.00638.
Raschka, S., & Mirjalili, V. (2007). Python machine learning (2nd ed.). Birmingham: Packt Publishing Ltd.
Scholkopf, B., Burges, C. J. C., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V. N. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing,45(11), 2758–2765. https://doi.org/10.1109/78.650102.
Shea, K., Ertelt, C., Gmeiner, T., Ameri, F., & Cam, C. A. D. C. (2010). Design-to-fabrication automation for the cognitive machine shop. Advanced Engineering Informatics,24(3), 251–268. https://doi.org/10.1016/j.aei.2010.05.017.
Silvén, O., Niskanen, M., & Kauppinen, H. (2003). Wood inspection with non-supervised clustering. Machine Vision and Applications,13(5–6), 275–285. https://doi.org/10.1007/s00138-002-0084-z.
Strese, M., Schuwerk, C., Iepure, A., & Steinbach, E. (2017). Multimodal feature-based surface material classification. IEEE Transactions on Haptics,10(2), 226–239. https://doi.org/10.1109/TOH.2016.2625787.
Tarlak, F., Ozdemir, M., & Melikoglu, M. (2016). Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods. Food Science and Technology,36(3), 499–504. https://doi.org/10.1590/1678-457x.02616.
Vejdannik, M., & Sadr, A. (2018). Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals. Journal of Intelligent Manufacturing,29(8), 1923–1940. https://doi.org/10.1007/s10845-016-1225-y.
Wen, C., Ph, D., & Chou, C. (2004). Color image models and its applications to document examination. Forensic Science Journal,3, 23–32.
Woods, D. D. (1985). Cognitive technologies: the design of joint human-machine cognitive systems. THE AI MAGAZINE,6(4), 86–92.
Zhao, Y. F., & Xu, X. (2010). Enabling cognitive manufacturing through automated on-machine measurement planning and feedback. Advanced Engineering Informatics,24(3), 269–284. https://doi.org/10.1016/j.aei.2010.05.009.
Acknowledgements
This work is carried out with the financial grant of Interdisciplinary Cyber-Physical Systems (ICPS) Division of the Department of Science and Technology, Government of India, Grant No.: DST/ICPS/CPS-Individual/2018/769(G), dated. 18-12-2018.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Penumuru, D.P., Muthuswamy, S. & Karumbu, P. Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. J Intell Manuf 31, 1229–1241 (2020). https://doi.org/10.1007/s10845-019-01508-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-019-01508-6