Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace
The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form...
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MDPI AG
2021-03-01
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doaj-684b5ef404cd40fd9cf6d6e17ff1681f2021-03-06T00:01:10ZengMDPI AGSensors1424-82202021-03-01211797179710.3390/s21051797Intelligent Dynamic Identification Technique of Industrial Products in a Robotic WorkplaceJán Vachálek0Dana Šišmišová1Pavol Vašek2Jan Rybář3Juraj Slovák4Matej Šimovec5Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, SlovakiaFaculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, SlovakiaSOVA Digital a.s. Bojnická 3, 831 04 Bratislava, SlovakiaFaculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, SlovakiaFaculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, SlovakiaFaculty of Mechanical Engineering, Slovak University of Technology in Bratislava, Námestie Slobody 17, 812 31 Bratislava, SlovakiaThe article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.https://www.mdpi.com/1424-8220/21/5/1797production linecolor sensoruncertaintiescontrol chartsmachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ján Vachálek Dana Šišmišová Pavol Vašek Jan Rybář Juraj Slovák Matej Šimovec |
spellingShingle |
Ján Vachálek Dana Šišmišová Pavol Vašek Jan Rybář Juraj Slovák Matej Šimovec Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace Sensors production line color sensor uncertainties control charts machine learning |
author_facet |
Ján Vachálek Dana Šišmišová Pavol Vašek Jan Rybář Juraj Slovák Matej Šimovec |
author_sort |
Ján Vachálek |
title |
Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace |
title_short |
Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace |
title_full |
Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace |
title_fullStr |
Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace |
title_full_unstemmed |
Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace |
title_sort |
intelligent dynamic identification technique of industrial products in a robotic workplace |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-03-01 |
description |
The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated. |
topic |
production line color sensor uncertainties control charts machine learning |
url |
https://www.mdpi.com/1424-8220/21/5/1797 |
work_keys_str_mv |
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