Development and Verification of an Original Method for Determining the Optimised Measurement Time with an Inspection Probe on a CNC Machine
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Leleń Michał, Kulisz Monika, Barszcz Marcin, Józwik Jerzy, Pieśko Paweł, Cybul Kamil, Sałamacha Daria, Zawada-Michałowska Magdalena |
| Dyscypliny: | |
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| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Strony: | 88 - 93 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| 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: | TAK |
| Nazwa konferencji: | 11th International Workshop on Metrology for AeroSpace |
| Skrócona nazwa konferencji: | 11th MetroAeroSpace 2024 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 3 czerwca 2024 do 5 lipca 2024 |
| Miasto konferencji: | Lublin |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The paper presents the results of temperature measurements after machining samples made of AW-2024 aluminum alloy and S235 steel. The temperature was measured at eight measurement points of examples. The machining process was performed using variable cutting parameters, i.e. wall thickness after machining, cutting depth and cutting speed. The results estimation was made based on the statistical analysis of the obtained measurements. The average values were used to determine the time courses of cooling curves. To perform calculations and numerical simulation, the artificial neural network algorithm of the Matlab 2023b package was used. Thanks to this, the time after which the workpiece can be measured with an inspection probe was determined. As a result, the measurement results showed that the use of artificial neural networks allows for accurate prediction of the start time of the probe measurement, which improves the efficiency of the process and minimizes prediction errors. |
