Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Sun, Yuqing" wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    A tool wear condition monitoring approach for end milling based on numerical simulation
    Autorzy:
    Zhu, Qinsong
    Sun, Weifang
    Zhou, Yuqing
    Gao, Chen
    Powiązania:
    https://bibliotekanauki.pl/articles/1841690.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    tool wear
    sample missing
    sample insufficiency
    numerical simulation
    cutting force
    Opis:
    As an important research area of modern manufacturing, tool condition monitoring (TCM) has attracted much attention, especially artificial intelligence (AI)- based TCM method. However, the training samples obtained in practical experiments have the problem of sample missing and sample insufficiency. A numerical simulation- based TCM method is proposed to solve the above problem. First, a numerical model based on Johnson-Cook model is established, and the model parameters are optimized through orthogonal experiment technology, in which the KL divergence and cosine similarity are used as the evaluation indexes. Second, samples under various tool wear categories are obtained by the optimized numerical model above to provide missing samples not present in the practical experiments and expand sample size. The effectiveness of the proposed method is verified by its application in end milling TCM experiments. The results indicate the classification accuracies of four classifiers (SVM, RF, DT, and GRNN) can be improved significantly by the proposed TCM method.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 2; 371-380
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Tytuł:
    Time-frequency Representation -enhanced Transfer Learning for Tool Condition Monitoring during milling of Inconel 718
    Autorzy:
    Zhou, Yuqing
    Sun, Wei
    Ye, Canyang
    Peng, Bihui
    Fang, Xu
    Lin, Canyu
    Wang, Gonghai
    Kumar, Anil
    Sun, Weifang
    Powiązania:
    https://bibliotekanauki.pl/articles/24200823.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    tool condition monitoring
    time-frequency analysis
    Markov Transition Field
    transfer learning
    Opis:
    Accurate tool condition monitoring (TCM) is important for the development and upgrading of the manufacturing industry. Recently, machine-learning (ML) models have been widely used in the field of TCM with many favorable results. Nevertheless, in the actual industrial scenario, only a few samples are available for model training due to the cost of experiments, which significantly affects the performance of ML models. A time-series dimension expansion and transfer learning (TL) method is developed to boost the performance of TCM for small samples. First, a time-frequency Markov transition field (TFMTF) is proposed to encode the cutting force signal in the cutting process to two-dimensional images. Then, a modified TL network is established to learn and classify tool conditions under small samples. The performance of the proposed TFMTF-TL method is demonstrated by the benchmark PHM 2010 TCM dataset. The results show the proposed method effectively obtains superior classification accuracies for small samples and outperforms other four benchmark methods.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 165926
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies