Product Classification Using Neural Network at Industry Robotic Line-Reference-Cited by-同舟云学术

Product Classification Using Neural Network at Industry Robotic Line

Published:2019-09-01 Issue:45 Volume:27 Page:55-63
ISSN:1338-0532
Container-title:Research Papers Faculty of Materials Science and Technology Slovak University of Technology
language:en
Short-container-title:

Author:

Halenár Igor¹,Križanová Gabriela¹

Affiliation:

1. Slovak University of Technology in Bratislava , Faculty of Materials Science and Technology in Trnava, Institute of Applied Informatics, Automation and Mechatronics , Ulica Jána Bottu Č. 2781/25, 917 24 Trnava , Slovak Republic

Abstract

Abstract The article describes a possible way of implementing a neural network in recognizing the shape and position of the products in the production process. The neural network is designed as a multilayer perceptron (MLP), and the whole system is implemented in a form of attachment to robotic arm, where the primary task of neural network is to distinguish a position of product. The neural network is trained like a classifier and outputs are used to control the robot. The advantage of the solution is a high degree of reliability of product positioning under different lighting conditions.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/rput-2019-0026

Reference13 articles.

1. [1] SPASOJEVIĆ, S. S., ŠUŠIĆ, M. Z., DUROVIĆ, Ž. M. 2012. Recognition and classification of geometric shapes using neural networks. In: 11th Symposium on Neural Network Applications in Electrical Engineering. NEUREL-2012, Faculty of Electrical Engineering, University of Belgrade, Serbia, pp. 71-76. ISBN 978-1-4673-1572-2.

2. [2] YANG, Y., ZHENG, P., HE, H., ZHENG, T., WANG, L., HE, S. 2018. An Evaluation Method of Acceptable and Failed Spot Welding Products Based on Image Classification with Transfer Learning Technique. In: ACM International Conference Proceeding Series. 2nd International Conference on Computer Science and Application Engineering: CSAE 2018, Hohhot China, a109. ISBN 978-145036512-3.

3. [3] TRAN, H. N., NGUYEN, T. 2014. Supervised learning method for mobile international number plate recognition. In: ACM International Conference Proceeding Series. 16th International Conference on Information Integration and Web-Based Applications and Services: iiWAS 2014, Hanoi, Viet Nam, pp. 212-216. ISBN 978-145033001-5.

4. [4] MARETA, A., SOESANTI, I., WAHYUNGGORO, O. 2018. Herbal Leaf Classification Using Images in Natural Background. In: 2018 International Conference on Information and Communications Technology. 1st International Conference on Information and Communications Technology: ICOIACT 2018, Grand Zuri, Yogyakarta, Indonesia, pp. 612-616. ISBN 978-153860954-5.

5. [5] MENG, F., ZHENG, H., YU, Z. 2011. Extracting and Recognition of Print Code on Beer Bottle Based on Regional Analysis. In: 2011 International Conference on Electronics, Communications and Control. ICECC 2011, Ningbo, China, pp. 3746-3749. ISBN 978-145770321-8.