Tool Wear Prediction Based on DRSN-BiLSTM Model

doi:10.3901/JME.2024.24.066

Abstract

Abstract: The prediction of tool wear in CNC machine tools is of great significance to improve the safety of tool processing and product processing quality. With advances in sensing technology, the amount of sensor data used for equipment condition monitoring in the manufacturing industry has exploded. This has led to a high priority for a deep learning-centric, data-driven approach in the field of tool wear prediction. However, it remains a challenge to accurately identify and extract features closely related to tool degradation and make the most of this information to improve the performance of predictive models. In order to solve the above problems, a tool wear prediction method based on deep learning was studied, and the multi-channel screening mechanism was applied to the prediction model, and a tool wear prediction method based on the deep residual shrinkage network-bidirectional long short term memory (DRSN-BiLSTM) model was proposed. According to the fluctuation degree of the monitoring signal, multiple channels related to the tool degradation height are selected for fusion, the convolutional channel attention mechanism is used to fuse the multi-channel data and efficiently mine the abstract feature information of each channel, and then the BiLSTM regression model is established to extract the time series information related to the tool wear to accurately predict the tool wear. Experiments verify the effectiveness of the channel screening mechanism and the accuracy of the prediction model.

Key words: tool wear, multi-channel fusion, deep learning, DRSN, BiLSTM

CLC Number:

TH17

SHI Lichen, SHI Weichun, WANG Haitao, LI Jinyang, LIU Yaxiong. Tool Wear Prediction Based on DRSN-BiLSTM Model[J]. Journal of Mechanical Engineering, 2024, 60(24): 66-74.

References

[1] REHORN G，JIANG J，ORBAN E. State-of-the-art methods and results in tool condition monitoring：A review[J]. The International Journal of Advanced Manufacturing Technology，2005，26(7-8)：693-710.
[2] AHMAD M I，YUSOF Y，DAUD M E，et al. Machine monitoring system：A decade in review[J]. The International Journal of Advanced Manufacturing Technology，2020，108：3645-3659.
[3] 李宏坤，郝佰田，代月帮，等. 基于压缩感知和加噪堆栈稀疏自编码器的铣刀磨损程度识别方法研究[J]. 机械工程学报，2019，55(14)：1-10.LI Hongkun，HAO Baitian，DAI Yuebang，et al. Wear status recognition for milling cutter based on compressed sensing and noise stacking sparse auto-encoder[J]. Journal of Mechanical Engineering，2019，55(14)：1-10.
[4] 叶文昌，郭必成，邓朝晖，等. 刀具智能化关键技术的研究进展及发展趋势[J]. 机械工程学报，2023，59(23)：265-282.YE Wenchang，GUO Bicheng，DENG Zhaohui，et al. Advances in key technologies of the intelligence tool[J]. Journal of Mechanical Engineering，2023，59(23)：265-282.
[5] WANG X，SONG Q，LIU Z. Precise chatter monitoring of thin-walled component milling process based on parametric time-frequency transform method[J]. Journal of Materials Processing Technology，2020，283：116712.
[6] PENG Y，SONG Q，WANG R，et al. Intelligent recognition of tool wear in milling based on a single sensor signal[J]. The International Journal of Advanced Manufacturing Technology，2023，124：1077-1093
[7] 杨国葳，李宏坤，张明亮，等. 基于一维深度卷积自动编码器的刀具状态监测方法[J]. 振动与冲击，2021，40(21)：223-233，274.YANG Guowei，LI Hongkun，ZHANG Mingliang，et al. Tool condition monitoring method based on ODCAE[J]. Journal of Vibration and Shock，2021，40(21)：223-233，274.
[8] 车畅畅，王华伟，倪晓梅，等. 基于1D-CNN和Bi-LSTM的航空发动机剩余寿命预测[J]. 机械工程学报，2021，57(14)：304-312.CHE Changchang，WANG Huawei，NI Xiaomei，et al. Residual life prediction of aeroengine based on 1D-CNN and Bi-LSTM[J]. Journal of Mechanical Engineering，2021，57(14)：304-312.
[9] WANG H，GUO Y，LIU X，et al. Fault diagnosis method for imbalanced data of rotating machinery based on time domain signal prediction and SC-ResNeSt[J]. IEEE Access，2023，11：38875-38893.
[10] WANG H，YANG J，SHI L，et al. Remaining useful life prediction based on adaptive SHRINKAGE processing and temporal convolutional network[J]. Sensors，2022，22(23)：9088.
[11] 曹大理，孙惠斌，张纪铎，等. 基于卷积神经网络的刀具磨损在线监测[J]. 计算机集成制造系统，2020，26(1)：74-80.CAO Dali，SUN Huibin，ZHANG Jiduo，et al. In-process tool condition monitoring based on convolution neural network[J]. Computer Integrated Manufacturing Systems，2020，26(1)：74-80.
[12] 吴飞，农皓业，马晨浩. 基于粒子群优化算法-长短时记忆模型的刀具磨损预测方法[J]. 吉林大学学报，2023，53(4)：989-997.WU Fei，NONG Haoye，MA Chenhao. Tool wear prediction method based on particle swarm optimization long and short time memory model[J]. Journal of Jilin University，2023，53(4)：989-997.
[13] 林京，焦金阳. 可解释机械智能诊断技术的研究进展与挑战[J]. 机械工程学报，2023，59(20)：215-224.LIN Jing，JIAO Jinyang. Research progress and challenges of interpretable mechanical intelligent diagnosis[J]. Journal of Mechanical Engineering，2023，59(20)：215-224.
[14] XU X，WANG J，ZHONG B，et al. Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism[J]. Measurement，2021，177： 109254.
[15] GUO H，ZHU K. Attention-based dual-scale hierarchical LSTM for tool wear monitoring[J]. Manufacturing Letters，2021，29：99-103.
[16] WANG R，SONG Q，PENG Y，et al. A milling tool wear monitoring method with sensing generalization capability[J]. Journal of Manufacturing Systems，2023，68：25-41.
[17] WANG R，SONG Q，PENG Y，et al. Self-adaptive fusion of local-temporal features for tool condition monitoring：A human experience free model[J]. Mechanical Systems and Signal Processing，2023，195：110310.
[18] 段暕，周宏娣，刘智勇，等. 基于改进PCANet模型的铣刀磨损预测方法研究[J]. 机械工程学报，2023，59(1)：278-285.DUAN Jian，ZHOU Hongdi，LIU Zhiyong，et al. Milling tool wear prediction research based on optimized PCANet model[J]. Journal of Mechanical Engineering，2023，59(1)：278-285.
[19] ZHANG X，SHI B，FENG B，et al. A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data[J]. Measurement，2023，213：112739.
[20] 李乃鹏，蔡潇，雷亚国，等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报，2021，57(20)：29-37，46.LI Naipeng，CAI Xiao，LEI Yaguo，et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery[J]. Journal of Mechanical Engineering，2021，57(20)：29-37，46.
[21] YUE D，YUE C，LIU X，et al. Analysis of cutting performance of the tool based on FEM and grey-fuzzy analytic hierarchy process[J]. The International Journal of Advanced Manufacturing Technology，2022，118(7-8)：2745-2758.
[22] HE K，ZHANG X，REN S，et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas，NV，USA：IEEE，2016：770-778.
[23] LUO B，WANG H，LIU H，et al. Early fault detection of machine tools based on deep learning and dynamic identification[J]. IEEE Transactions on Industrial Electronics，2018，66(1)：509-518.
[24] 中华人民共和国国家质量监督检验检疫总局，中国国家标准化管理委员会. GB/T 16461-2016单刃车削刀具寿命试验[S]. 北京：中国标准出版社，2016.General Administration of Quality Supervision，Inspection and Quarantine of the People’s Republic of China，Standardization Administration of the People’s Republic of China. GB/T 16461-2016 Tool-life testing with single-point turning tools[S]. Beijing：Standards Press of China，2016.
[25] BAZI R，BENKEDJOUH T，HABBOUCHE H，et al. A hybrid CNN-BILSTM approach-based variational mode decomposition for tool wear monitoring[J]. The International Journal of Advanced Manufacturing Technology，2022，119(5-6)：3803-3817.