Modeling of optimal Probe Measurement Time on Amachine Tool Using Machine Learning Methods
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Józwik Jerzy, Zawada-Michałowska Magdalena, Kulisz Monika, Tomiło Paweł, Barszcz Marcin, Pieśko Paweł, Leleń Michał, Cybul Kamil |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2024 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 2 |
| Wolumen/Tom: | 20 |
| Strony: | 43 - 59 |
| Bazy: | BazTech | Cabell's Directory | Central & Eastern European Academic Source (CEEAS) | CNKI Scholar (China National Knowledge Infrastucture) |
| Efekt badań statutowych | NIE |
| Finansowanie: | This work was prepared within the project PM/SP/0063/2021/1 titled “Innovative measurement technologies supported by digital data processing algorithms for improved processes and products”, financed by the Ministry of Education and Science (Poland) as a part of the Polish Metrology Programme. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 30 czerwca 2024 |
| Abstrakty: | angielski |
| This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines theinput parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes |
