| Autor: |
Dany Katamba Mpoyi, Aimé Lay Ekuakille, Moise Avoci Ugwiri, Caterina Casavola, Giovanni Pappalettera |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Measurement: Sensors, Vol 32, Iss , Pp 101051- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2665-9174 |
| DOI: |
10.1016/j.measen.2024.101051 |
| Popis: |
This study explores the effectiveness of empirical mode decomposition (EMD) and filter diagonalization methods (FDM) in joint monitoring tool wear during machining operations using vibration measurements. The research investigates diverse tools and working conditions to comprehensively evaluate these techniques. Statistical analysis employing Kurtosis, Skewness, and RMS is used to select relevant intrinsic modes, with IMF 4 identified as the optimal mode for tracking tool wear. The validity of the EMD results is confirmed through Fast Fourier Transform (FFT) analysis, which further supports the efficacy of EMD in tool wear monitoring. Additionally, FDM, a frequency-domain signal processing tool commonly used in nuclear magnetic resonance, is applied for the first time to track wear. While FDM may face challenges in resolving certain peaks, it provides stable and cumulative intensities, helping in local spectral resolution. The results should be interpreted cautiously, with further research and validation needed. Overall, this study demonstrates the potential of EMD and FDM methods for practical implementation in accurate and reliable tool wear assessment during machining operations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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