Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Leleń Michał, Biruk-Urban Katarzyna, Józwik Jerzy, Tomiło Paweł
Dyscypliny:
	Aby zobaczyć szczegóły należy się zalogować.
Rok wydania:	2023
Wersja dokumentu:	Drukowana \| Elektroniczna
Język:	angielski
Numer czasopisma:	19
Wolumen/Tom:	16
Numer artykułu:	6474
Strony:	1 - 30
Impact Factor:	3,1
Web of Science® Times Cited:	1
Scopus® Cytowania:	5
Bazy:	Web of Science \| Scopus
Efekt badań statutowych	NIE
Finansowanie:	The activities of the Polish Metrological Union are financed from the funds of the Ministry of Education and Science as part of a targeted subsidy for the implementation of the task titled ”Es- tablishment and Coordination of the activities of the Polish Metrological Union (PMU)” under con- tract No. MEiN /2021/DPI/179. This research was partially supported by the Mechanical Engineering Discipline Fund of Lublin University of Technology (Grant No. FD-20/IM-5/050).
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:	26 września 2023
Abstrakty:	angielski
	This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, vfi; and pressure, pi), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was developed using the obtained maximum acceleration values and surface roughness parameters (Ra, Rz, and RSm). In this study, five different models were adopted. Due to the characteristics of the data, five regression methods were selected: Random Forest Regressor, Linear Regression, Gradient Boosting Regressor, LGBM Regressor, and XGBRF Regressor. The maximum vibration amplitude reached the lowest acceleration value for aluminum alloy (not exceeding 5 m/s2), indicating its susceptibility to cutting while maintaining a high surface quality. However, significantly higher acceleration amplitudes (up to 60 m/s2) were registered for steel and titanium alloy in all process zones. The predicted roughness parameters were determined from the developed models using second-degree regression equations. The prediction of vibration parameters and surface quality estimators after waterjet cutting can be a useful tool that for allows for the selection of the optimal abrasive waterjet machining (AWJM) technological parameters.

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Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting

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