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Letter

Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes

1
Institute of Information Technology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
2
Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
3
Department of Artificial Intelligence, Institute of Information Technology, Warsaw University of Technology, 02-776 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6978; https://doi.org/10.3390/s20236978
Received: 6 November 2020 / Revised: 3 December 2020 / Accepted: 3 December 2020 / Published: 6 December 2020
In this article, a Siamese network is applied to the drill wear classification problem. For furniture companies, one of the main problems that occurs during the production process is finding the exact moment when the drill should be replaced. When the drill is not sharp enough, it can result in a poor quality product and therefore generate some financial loss for the company. In various approaches to this problem, usually, three classes are considered: green for a drill that is sharp, red for the opposite, and yellow for a tool that is suspected of being worn out, requiring additional evaluation by a human expert. In the above problem, it is especially important that the green and the red classes not be mistaken, since such errors have the highest probability of generating financial loss for the manufacturer. Most of the solutions analysing this problem are too complex, requiring specialized equipment, high financial investment, or both, without guaranteeing that the obtained results will be satisfactory. In the approach presented in this paper, images of drilled holes are used as the training data for the Siamese network. The presented solution is much simpler in terms of the data collection methodology, does not require a large financial investment for the initial equipment, and can accurately qualify drill wear based on the chosen input. It also takes into consideration additional manufacturer requirements, like no green-red misclassifications, that are usually omitted in existing solutions. View Full-Text
Keywords: Siamese network; contrastive loss function; convolutional neural networks; deep learning; tool condition monitoring Siamese network; contrastive loss function; convolutional neural networks; deep learning; tool condition monitoring
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MDPI and ACS Style

Kurek, J.; Antoniuk, I.; Świderski, B.; Jegorowa, A.; Bukowski, M. Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes. Sensors 2020, 20, 6978. https://doi.org/10.3390/s20236978

AMA Style

Kurek J, Antoniuk I, Świderski B, Jegorowa A, Bukowski M. Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes. Sensors. 2020; 20(23):6978. https://doi.org/10.3390/s20236978

Chicago/Turabian Style

Kurek, Jarosław, Izabella Antoniuk, Bartosz Świderski, Albina Jegorowa, and Michał Bukowski. 2020. "Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes" Sensors 20, no. 23: 6978. https://doi.org/10.3390/s20236978

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