Abstract
Industry 4.0 and its adaptation is no longer a good to have enabler for all manufacturing industry now. Usage of artificial intelligence in perspective of Industry 4.0 is penetrative and the horizon vast. In today’s world, developing an automated system for identification of fracture and its location is an emerging research area in automobile industries. A proper identification of fractures at the time of manufacturing of vehicles leads to both time and economical reclaims with excellent accuracy. In this paper, an automated system is implemented which is able to identify the features with its location with high accuracy. For this, ‘OpenCV’ resource is used for development of algorithm and de-noising process to extract the features. A machine learning technique, supported by region-based convolutional neural network (R-CNN) is implemented, and a transfer learning mechanism is used by which the system learns itself from raw data and classifies it with a good accuracy.
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Mohapatra, D., Chakraborty, A., Shaw, A.K. (2021). Exploring Novel Techniques to Detect Aberration from Metal Surfaces in Automobile Industries. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_5
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DOI: https://doi.org/10.1007/978-981-33-4866-0_5
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