A MONITORING METHOD OF MILLING CHATTER BASED ON OPTIMIZED HYBRID NEURAL NETWORK WITH ATTENTION MECHANISM

Haining Gao, Hongdan Shen, Caixu Yue, Rongyi Li, Steven Y. Liang, Yinlin Wang, Wenfu Liu, Yong Yang

DOI Number
10.22190/FUME240804047G
First page
Last page

Abstract


Machining chatter is a self-excited vibration between the cutting tool and the workpiece, which can reduce surface quality and tool life, and even endanger the safety of operators in severe cases. Considering that milling chatter has multi-scale features and the debugging of neural network hyperparameters heavily relies on experience, a milling chatter monitoring method based on an optimized hybrid neural network with an attention mechanism (MISSA-MSCNN-BiLSTM-ATM) is proposed. Firstly, the harmonic of the spindle rotation frequency is filtered out using the spindle rotation frequency removal technique (SFT). Then, an improved sparrow search algorithm (MISSA) is proposed based on multiple strategies including improved circle chaotic mapping, golden sine strategy, and enhanced Lévy flight. Subsequently, MISSA is utilized to optimize the hyperparameters of the milling chatter classification hybrid neural network model, combining multi-scale convolutional neural networks (MSCNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (ATM). In numerical simulations with CEC2005 complex functions, MISSA demonstrates better optimization accuracy, stability, and shorter computation time compared to other intelligent algorithms. Compared with other milling chatter classification models, the proposed method exhibits significant improvements in accuracy and stability.


Keywords

Chatter monitoring, Attention mechanism, Hybrid neural network, Optimization algorithm

Full Text:

PDF

References


Rahimi, M. H., Huynh, H. N., Altintas, Y., 2021, On-line chatter detection in milling with hybrid machine learning and physics-based model, CIRP Journal of Manufacturing Science and Technology, 35, pp. 25-40.

Jeong, K., Kim, W., Kim, N., Park, J., 2023, Chatter detection in milling process with feature selection based on sub-band attention convolutional neural network, The International Journal of Advanced Manufacturing Technology, 128(1-2), pp. 181-196.

Yesilli, M.C., Khasawneh, F.A., Otto, A., 2022, Chatter detection in turning using machine learning and similarity measures of time series via dynamic time warping, Journal of Manufacturing Processes, 77, pp. 190-206.

Matthew, D.E., Cao, H., Shi, J., 2024, Advancing chatter detection: Harnessing the strength of wavelet synchrosqueezing transform and Hilbert-Huang transform techniques, Journal of Manufacturing Processes, 127, pp. 613-630.

Li, K., He, S., Li, B., Liu, H., Mao, X., Shi, C., 2020, A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting, Mechanical Systems and Signal Processing, 135, 106385.

Tran, M.Q., Liu, M.K., Tran, Q.V., 2020, Milling chatter detection using scalogram and deep convolutional neural network, The International Journal of Advanced Manufacturing Technology, 107(3), pp. 1505-1516.

Sun, Y., He, J., Ma, H., Yang, X., Xiong, Z., Zhu, X., Wang, Y., 2023, Online chatter detection considering beat effect based on Inception and LSTM neural networks, Mechanical Systems and Signal Processing, 184, 109723.

Aslan, D., Altintas, Y., 2018, On-line chatter detection in milling using drive motor current commands extracted from CNC, International Journal of Machine Tools and Manufacture, 32, pp. 64-80.

Zheng, X., Arrazola, P., Perez, R., Echebarria, D., Kiritsis, D., Aristimuño, P., Sáez-de-Buruaga, M., 2023, Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process, Mechanical Systems and Signal Processing, 185, 109812.

Li, G., Bao, Y., Wang, H., Dong, Z., Guo, X., Kang, R., 2023, An online monitoring methodology for grinding state identification based on real-time signal of CNC grinding machine, Mechanical Systems and Signal Processing, 200, 110540.

Sestito, G.S., Venter, G.S., Ribeiro, K.S.B., Rodrigues, A. R., Silva, M.,2022. In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers, The International Journal of Advanced Manufacturing Technology, 120(11), pp.7293-7303.

Bakhshandeh, P., Mohammadi, Y., Altintas, Y., Bleicger, F., 2024, Digital twin assisted intelligent machining process monitoring and control, CIRP Journal of Manufacturing Science and Technology, 49, pp. 180-190.

Gao, H., Shen, H., Yu, L., Yinling, W., Li, R., Nazir, B., 2021, Milling chatter detection system based on multi-sensor signal fusion, IEEE Sensors Journal, 21(22), pp. 25243-25251.

Tran, M. Q., Liu, M. K., Elsisi, M., 2022, Effective multi-sensor data fusion for chatter detection in milling process, ISA transactions, 125, pp. 514-527.

Zhou, G., Zhou, K., Zhang, J., Yuan, M., Wang, X., Feng, P., Zhang, M., Feng, F., 2024, Digital modeling-driven chatter suppression for thin-walled part manufacturing, Journal of Intelligent Manufacturing, 35(1), pp. 289-305.

Yin, C., Wang, Y., Ko, J. H., Lee, H. P., Sun, Y., 2024, Attention-driven transfer learning framework for dynamic model guided time domain chatter detection. Journal of Intelligent Manufacturing, 35(4), pp. 1867-1885.

Kuo, P.H., Luan, P.C., Tseng, Y.R., Yau, H.T., 2023, Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets, Structural Health Monitoring, 22(6), pp. 3891-3907.

Albertelli, P., Braghieri, L., Torta, M., Monno, M., 2019, Development of a generalized chatter detection methodology for variable speed machining, Mechanical Systems and Signal Processing, 123, pp. 26-42.

Yan, S.C., Sun, Y.W., 2022, Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection, Mechanical Systems and Signal Processing, 169, 108622.

Wan, S., Liu, S., Li, X., Yan, K., Hong, J., 2023, Milling chatter detection based on information entropy of interval frequency, Measurement, 220, 113328.

Matthew, D.E., Shi, J., Hou, M., Cao, H., 2024, Improved STFT analysis using time-frequency masking for chatter detection in the milling process, Measurement, 225, 113899.

Li, D., Du, H., Yip, W.S., Tang, Y. M., To, S., 2024, Online chatter detection for single-point diamond turning based on multidimensional cutting force fusion, Mechanical Systems and Signal Processing, 206, 110850.

Wan, M., Wang, W. K., Zhang, W. H., Yang, Y., 2023, Chatter detection for micro milling considering environment noises without the requirement of dominant frequency, Mechanical Systems and Signal Processing, 199, 110451.

Thaler, T., Krese, B., Govekar, E., 2015, Stability diagrams and chatter avoidance in horizontal band sawing, CIRP annals, 64(1), pp. 81-84.

Wan, S., Li, X., Yin, Y., Hong, J., 2021, Milling chatter detection by multi-feature fusion and Adaboost-SVM, Mechanical Systems and Signal Processing, 156, 107671.

Sener, B., Gudelek, M.U., Ozbayoglu, A.M., Unver, H. O., 2021, A novel chatter detection method for milling using deep convolution neural networks, Measurement, 182, 109689.

Yesilli, M.C., Khasawneh, F.A., Otto, A., 2020, On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition, CIRP Journal of Manufacturing Science and Technology, 28, pp. 118-135.

Yang, B., Guo, K., Zhou, Q., Sun, J., 2023, Early chatter detection in robotic milling under variable robot postures and cutting parameters, Mechanical Systems and Signal Processing, 186, 109860.

Lu, Y., Ma, H., Sun, Y., Liu, Z., Song, Q., 2022, An early chatter detection method based on multivariate variational mode decomposition and chatter correlation factor, IEEE/ASME Transactions on Mechatronics, 27(6), pp. 5724-5735.

Jauhari, K., Rahman, A. Z., Al, Huda, M., Azka, M., Widodo, A., Prahasto, T., 2024, A feature extraction method for intelligent chatter detection in the milling process, Journal of Intelligent Manufacturing, pp. 1-27.

Jauhari, K., Rahman, A. Z., Al, Huda, M., Azka, M., Widodo, A., Prahasto, T., 2023, Building digital-twin virtual machining for milling chatter detection based on VMD, synchro-squeeze wavelet, and pre-trained network CNNs with vibration signals, Journal of Intelligent Manufacturing, pp. 1-32.

Chen, K., Zhang, X., Zhao, W., 2023, Automatic feature extraction for online chatter monitoring under variable milling conditions, Measurement, 210, 112558.

Zhao, Y., Adjallah, K.H., Sava, A., Wang, Z., 2022, Incipient chatter fast and reliable detection method in high-speed milling process based on cumulative strategy, ISA transactions, 131, pp. 397-414.

Yesilli, M.C., Khasawneh, F.A., Mann, B.P., 2022, Transfer learning for autonomous chatter detection in machining, Journal of Manufacturing Processes, 80, pp. 1-27.

Kounta, C.A.K.A., Arnaud, L., Kamsu, F.B., Tangara, F., 2023, Deep learning for the detection of machining vibration chatter, Advances in Engineering Software, 180, 103445.

Unver, H.O., Sener, B., 2023, A novel transfer learning framework for chatter detection using convolutional neural networks, Journal of Intelligent Manufacturing, 34(3), pp. 1105-1124.

Xue, J., Shen, B., 2020, A novel swarm intelligence optimization approach: Sparrow search algorithm, Systems science & control engineering, 8(1), pp. 22-34.

Nadimi-Shahraki, M.H., Taghian, S., Mirjalili, S., 2021, An improved grey wolf optimizer for solving engineering problems, Expert Systems with Applications, 166, 113917.

Li, J., Chen, J., Shi, J., 2023, Evaluation of new sparrow search algorithms with sequential fusion of improvement strategies, Computers & Industrial Engineering, 182, 109425.

Hashim, F.A., Hussien, A.G., 2022, Snake Optimizer: A novel meta-heuristic optimization algorithm, Knowledge-Based Systems, 242, 108320.

Abdollahzadeh, B., Gharehchopogh, F. S., Mirjalili, S., 2021, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Computers & Industrial Engineering, 158, 107408.


Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4