- Tytuł:
- Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
- Autorzy:
-
Zheng, Guoxiao
Sun, Weifang
Zhang, Hao
Zhou, Yuqing
Gao, Chen - Powiązania:
- https://bibliotekanauki.pl/articles/2038054.pdf
- Data publikacji:
- 2021
- Wydawca:
- Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
- Tematy:
-
tool wear condition monitoring
empirical mode decomposition
variational mode decomposition
fourier synchro squeezed transform
neighborhood component analysis
long short-term memory network - Opis:
- Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
- Źródło:
-
Eksploatacja i Niezawodność; 2021, 23, 4; 612-618
1507-2711 - Pojawia się w:
- Eksploatacja i Niezawodność
- Dostawca treści:
- Biblioteka Nauki
Artykuł