- author = "Baressi Segota Sandi and Mrzljak Vedran and Prpic-Orsic Jasna and Zlatan Car",
- title = "Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method",
- journal = "Industry 4.0",
- year = "2023",
- volume = "8",
- number = "1",
- pages = "21--24",
- note = "Dominant technologies in",
- keywords = "genetic algorithms, genetic programming, friction torque prediction, industrial robotic manipulator, friction, machine learning, symbolic regression",
- ISSN = "2534-8582",
-
URL = "
https://stumejournals.com/journals/i4/2023/1/21.full.pdf",
- size = "4 pages",
- abstract = "The goal of the paper is estimating the normalized friction torque of a joint in an industrial robotic manipulator. For this purpose a source data, given as a figure, is digitized using a tool WebPlotDigitizer in order to obtain numeric data. The numeric data is the used within the machine learning algorithm genetic programming (GP), which performs the symbolic regression in order to obtain the equation that regresses the dataset in question. The obtained model shows a coefficient of determination equal to 0.87, which indicates that the model in question may be used for the wide approximation of the normalized friction torque using the torque load, operating temperature and joint velocity as inputs.",
-
notes = "Published by Scientific Technical Union of Mechanical
Engineering 'industry 4.0'. 108, RakovskiStr., 1000
Sofia, Bulgaria. office@stumejournals.com
Faculty of Engineering, University of Rijeka, Croatia",
