Abstract
This paper addresses the utility of intelligent autonomous robotic arm for automatic removal of defective products in an industry. The task can be performed in two steps, finding the defective product with digital image processing and removal of defective part from the products. The image is regularly obtained and compared with the standard image. The defective product is sorted out based on threshold value between the real image and standard image. After detection of defective product, it is sorted out with the help of robotic arm and placed in the defective lot.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chang, C.-Y., Lin, S.-Y., Jeng, M.: Using a Two-layer Competitive Hopfield Neural Network for Semiconductor Wafer Defect Detection. In: Proceedings of the IEEE International Conference on Automation Science and Engineering, Edmonton, Canada, August 1-2, pp. 301–306 (2005)
Robinson, A.P., Lewin, P.L., Swingler, S.G.: Detection of Manufacturing Defects in Polymeric Cable Joint Insulation using X-rays. In: Conference Record of the IEEE International Symposium on Electrical Insulation, pp. 34–37 (2006)
Priya, S., Kumar, T.A., Paul, V.: A Novel Approach to Fabric Defect Detection Using Digital Image Processing. In: Proceedings of International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN 2011), pp. 228–232 (2011)
Clement, W.I.: An Instructional Robotics and Machine Vision Laboratory. IEEE Transactions on Education 37(I), 87–90 (1994)
Garcia, G.J., Pomares, J., Torres, F.: Automatic robotic tasks in unstructured environments using an image path tracker. Control Engineering Practice 17, 597–608 (2009)
Kelly, R., Carelli, R., Nasisi, O., Kuchen, B., Reyes, F.: Stable visual servoing of camera-in-hand robotic systems. IEEE-ASME Transactions on Mechatronics 5, 39–48 (2000)
Reyes, J.F., Chiang, L.: Location and Classification of Moving Fruits In Real Time with A Single Color Camera. Chilean Journal of Agricultural Research 69, 179–187 (2009)
Wang, H., Cao, Q., Masateru, N., Bao, J.: Image processing and robotic techniques in plug seedling production. Transactions of the Chinese Society of Agricultural Machinery 30, 57–62 (1999)
Zhang, Y., Wang, Y., Zuo, M.J., Wang, X.: Ultrasonic Time Of Flight Diffraction Crack Size Identification Based on Cross Correlation. In: Canadian Conference on Electrical and Computer Engineering CCECE/CCGEI IEEE, pp. 1737–1780 (2008)
Wang, C.-C., Jiang, B.C., Lin, J.-Y., Chu, C.-C.: Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient. IEEE Transactions on Semiconductor Manufacturing 26(3), 378–384 (2013)
Laddha, N.R., Thakare, A.P.: A Review on Serial Communication by UART. International Journal of Advanced Research in Computer Science and Software Engineering 3(1), 366–369 (2013)
Seelye, M.: Camera-in-hand robotic system for remote monitoring of plant growth in a laboratory. In: 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Singh, B., Chandra, M., Kandru, N. (2015). Removal of Defective Products Using Robots. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-12012-6_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12011-9
Online ISBN: 978-3-319-12012-6
eBook Packages: EngineeringEngineering (R0)