Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method

Baressi Šegota, Sandi; Mrzljak, Vedran; Prpić-Oršić, Jasna; Car, Zlatan

Pregled bibliografske jedinice broj: 1259390

Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method

Baressi Šegota, Sandi; Mrzljak, Vedran; Prpić-Oršić, Jasna; Car, Zlatan

Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method // XVI International Conference for Young Researchers "Technical Sciences. Industrial Management 2023" - PROCEEDINGS / Angelov, Cyril (ur.).
Sofija: Scientific technical union of mechanical engineering Industry - 4.0, 2023. str. 5-8 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

CROSBI ID: 1259390 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Determining normalized friction torque of an industrial robotic manipulator using the symbolic regression method

Autori
Baressi Šegota, Sandi ; Mrzljak, Vedran ; Prpić-Oršić, Jasna ; Car, Zlatan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
XVI International Conference for Young Researchers "Technical Sciences. Industrial Management 2023" - PROCEEDINGS / Angelov, Cyril - Sofija : Scientific technical union of mechanical engineering Industry - 4.0, 2023, 5-8

Skup
XVI International Conference for Young Researchers "Technical Sciences. Industrial Management 2023"

Mjesto i datum
Borovets, Bugarska, 08.03.2023. - 11.03.2023

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Genetic programming ; Friction torque prediction ; Industrial robotic manipulator friction ; Mechine learning ; Symbolic regression

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

POVEZANOST RADA

Projekti:
IP-2018-01-3739 - Sustav potpore odlučivanju za zeleniju i sigurniju plovidbu brodova (DESSERT) (Prpić-Oršić, Jasna, HRZZ - 2018-01) ( CroRIS)
NadSve-Sveučilište u Rijeci-UNIRI_TEHNIC‐18‐18‐1146 - Nesigurnosti procjene brzine broda u pri realnim vremenskim uvjetima (Prpić-Oršić, Jasna, NadSve ) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-14 - Optimizacija dizalica topline i rashladnih sustava koji koriste radne tvari niskog utjecaja na globalno zatopljenje korištenjem numeričkih simulacija (Pavković, Branimir, NadSve - NATJEČAJ „UNIRI PROJEKTI“ Natječaj za dodjelu sredstava potpore znanstvenim istraživanjima na Sveučilištu u Rijeci za 2018. godinu - projekti iskusnih znanstvenika i umjetnika od 03. 09. 2018.) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)