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Naslov:Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learning
Avtorji:ID Berus, Lucijano (Avtor)
ID Hernavs, Jernej (Avtor)
ID Potočnik, David (Avtor)
ID Šket, Kristijan (Avtor)
ID Ficko, Mirko (Avtor)
Datoteke:.pdf sensors-25-00169_(1).pdf (4,44 MB)
MD5: D88C43CFCA902089A3CB8B7A0CB45F50
 
URL https://www.mdpi.com/1424-8220/25/1/169
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Ključne besede:smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:11.12.2024
Datum sprejetja članka:16.12.2024
Datum objave:31.12.2024
Založnik:MDPI
Leto izida:2024
Št. strani:19 str.
Številčenje:Vol. 25, iss. 1, [article no.] 169
PID:20.500.12556/DKUM-91985 Novo okno
UDK:658.5:004.6
COBISS.SI-ID:225665795 Novo okno
DOI:10.3390/s25010169 Novo okno
ISSN pri članku:1424-8220
Datum objave v DKUM:10.03.2025
Število ogledov:0
Število prenosov:4
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
BERUS, Lucijano, HERNAVS, Jernej, POTOČNIK, David, ŠKET, Kristijan in FICKO, Mirko, 2024, Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learning. Sensors [na spletu]. 2024. Vol. 25, no. 1,  169. [Dostopano 9 april 2025]. DOI 10.3390/s25010169. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=91985
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Gradivo je del revije

Naslov:Sensors
Skrajšan naslov:Sensors
Založnik:MDPI
ISSN:1424-8220
COBISS.SI-ID:10176278 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P2-0157-2020
Naslov:Tehnološki sistemi za pametno proizvodnjo
Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:L2-3167-2021
Naslov:Kognitivna geometrijska kontrola mehansko obdelanih odkovkov na osnovi množičnih podatkov iz obdelovalnega procesa

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:pametni proizvodni stroji, podatkovno vodena proizvodnja, algoritmi strojnega učenja, podatki CNC krmilnika, geometrijska natančnost


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