人工智能技术在数控机床主轴系统的研究进展
DOI:
https://doi.org/10.52810/JIR.2024.002Keywords:
人工智能技术, 主轴系统, 热误差, 故障诊断Abstract
机床作为现代工业的制造主体，是关乎国家发展的工业基石，而主轴系统作为机床中最重要的部件，影响其精度的相关技术问题也尤为重要。文中主要从基于智能化模型的主轴系统热误差预测、补偿和故障诊断两方面展开讨论，分别讨论了各种智能化算法模型的技术路线与国内外研究进展，并对这些算法模型进行了对比分析，分别讨论了其泛化性、鲁棒性与应用效果。
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Chrzanowski J, Sałaciński T, Skiba P. Spindle Error Movements and Their Measurement[J]. Applied sciences. 2021, 11(10): 4571.
Lara De Leon M A, Kolarik J, Byrtus R, et al. Tool Condition Monitoring Methods Applicable in the Metalworking Process[J]. Archives of computational methods in engineering. 2024, 31(1): 221-242.
Lin C, Lin Y, Chu C. Dynamic models and design of spindle-bearing systems of machine tools: A review[J]. International journal of precision engineering and manufacturing. 2013, 14(3): 513-521.
Cao H, Li B, He Z. Chatter stability of milling with speed-varying dynamics of spindles[J]. International Journal of Machine Tools and Manufacture. 2011, 52(1): 50-58.
Altintas Y, Cao Y. Virtual Design and Optimization of Machine Tool Spindles[J]. CIRP Annals - Manufacturing Technology. 2005, 54(1): 379-382.
Gouarir A, Martínez-Arellano G, Terrazas G, et al. In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis[J]. Procedia CIRP. 2018, 77: 501-504.
Mori M, Fujishima M, Inamasu Y, et al. A study on energy efficiency improvement for machine tools[J]. CIRP Annals - Manufacturing Technology. 2011, 60(1): 145-148.
Aslan D, Altintas Y. On-line chatter detection in milling using drive motor current commands extracted from CNC[J]. International Journal of Machine Tools and Manufacture. 2018, 132: 64-80.
Denkena B, Bergmann B, Klemme H. Cooling of motor spindles—a review[J]. International journal of advanced manufacturing technology. 2020, 110(11-12): 3273-3294.
Li Y, Zhao W, Lan S, et al. A review on spindle thermal error compensation in machine tools[J]. International Journal of Machine Tools and Manufacture. 2015, 95: 20-38.
Liu Z, Yang B, Ma C, et al. Thermal error modeling of gear hobbing machine based on IGWO-GRNN[J]. The International Journal of Advanced Manufacturing Technology. 2020, 106(11): 5001-5016.
Grama S N, Mathur A, Badhe A N. A model-based cooling strategy for motorized spindle to reduce thermal errors[J]. International Journal of Machine Tools and Manufacture. 2018, 132: 3-16.
Zhaolong L, Benchao S, Wenming Z, et al. Thermal error modeling of motorized spindle and application of miniature radiator in motorized spindle[J]. International journal of advanced manufacturing technology. 2024.
魏新园，钱自强，吴秋源，等. 数据机理驱动的机床主轴热精度建模方法研究[J]. 仪器仪表学报. 2024.
郭世杰，张学炜，张楠，等. 机床主轴热关键点选择与典型转速热误差预测[J]. 吉林大学学报（工学版）. 2023, 53(1): 72-81.
Ouerhani N, Loehr B, Rizzotti-Kaddouri A, et al. Data-Driven Thermal Deviation Prediction in Turning Machine-Tool - A Comparative Analysis of Machine Learning Algorithms[J]. Procedia Computer Science. 2022, 200: 185-193.
李国龙，陈孝勇，李喆裕，等. 采用天鹰优化卷积神经网络的精密数控机床主轴热误差建模[J]. 西安交通大学学报. 2022, 56(8): 51-61.
Yang Y, Lv J, Xiao Y, et al. Enhanced modeling method of thermal behaviors in machine tool motorized spindles based on the mixture of thermal mechanism and machine learning[J]. Journal of Intelligent Manufacturing. 2023.
郑悦，付国强，雷国强，等. 变工况下基于迁移学习融合域内对齐的机床主轴热误差模型[J]. 仪器仪表学报. 2023, 44(5): 33-43.
涂怡蓉，陈秀梅，史晨阳，等. 数控机床主轴的神经网络热评价模型研究[J]. 机床与液压. 2020, 48(22): 24-28.
张恩忠，程亚平，齐月玲，等. 基于最小二乘支持向量机的精密数控机床热误差建模与补偿研究[J]. 机床与液压. 2018, 46(20): 7-10.
Zhao Z, Huang N, Shen Y, et al. Modeling and prediction of full-term thermal error in linear axis of machine tools based on MSTGCN-A[J]. International journal of advanced manufacturing technology. 2024, 130(9-10): 4805-4819.
Sun J, Liu Z, Qiu C, et al. An axial attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions[J]. International journal of advanced manufacturing technology. 2024, 130(3-4): 1405-1419.
Gao Y, Xia X, Guo Y. A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM[J]. Applied sciences. 2024, 14(1): 381.
曹利，彭骥，殷鸣，等. 基于MEA-BP算法的卧式加工中心主轴热误差建模[J]. 组合机床与自动化加工技术. 2022(7): 30-33, 37.
刘洪江，胡腾，何勇，等. 基于CSO-SVM的数控机床主轴热误差建模[J]. 工程设计学报. 2022, 29(3): 339-346.
石颜龙，田春苗，阿勇嘎，等. 混合蛙跳算法优化SVM的进给轴热误差预测研究[J]. 航空制造技术. 2021, 64(22): 48-55.
吴金文，王玉鹏，周海波. 基于模拟退火耦合粒子群算法优化BP神经网络的机床主轴热误差补偿研究[J]. 机床与液压. 2019, 47(11): 92-95.
Shengnan T, Shouqi Y, Yong Z. Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery[J]. IEEE Access. 2020, 8: 9335-9346.
樊红卫，张旭辉，寇发荣，等. 电主轴振动故障诊断与治愈研究进展[J]. 制造技术与机床. 2020(9): 56-59, 64.
李阗岐，李小虎，万少可，等. 智能主轴技术发展综述[J]. 轴承. 2023(1): 1-11.
杨磊. 基于SVM的机电一体化数控机床主轴故障预测技术[J]. 木工机床. 2023(1): 12-15.
魏许杰，王红军，邢济收，等. 基于CGA-SVR的电主轴磨损故障诊断方法研究[J]. 电子测量与仪器学报. 2022, 36(06): 107-112.
曾夏，张富强，邵树军，等. 基于FP-Growth算法的数控机床故障特征分析[J]. 机床与液压. 2022, 50(16): 174-180.
李振雨，王好臣，王功亮，等. 基于BP网络的机床主轴故障诊断研究[J]. 机械设计与制造. 2019(10): 130-133, 139.
Xue R, Zhang P, Huang Z, et al. Digital twin ‑ driven fault diagnosis for CNC machine tool[J]. The International Journal of Advanced Manufacturing Technology. 2022.
Zhang Y, Li Y, Kong L, et al. Improved DBSCAN Spindle Bearing Condition Monitoring Method Based on Kurtosis and Sample Entropy[J]. Machines (Basel). 2022, 10(5): 363.
刘斌，刘佳，张海鹏. 基于经验模态分析的机床主轴轴承外圈非接触式故障检测方法[J]. 制造技术与机床. 2023(1): 21-28.
王振亚，伍星，刘韬，等. 奇异谱分解联合互信息的主轴轴承故障特征提取研究[J]. 振动与冲击. 2023, 42(15): 23-30, 47.
王寿元，李积元，郎永存，等. 基于PSO优化SVM数控机床主轴系统故障诊断的研究[J]. 组合机床与自动化加工技术. 2023(9): 151-155, 159.
李坤宏，江桂云，朱代兵. 数控机床电动主轴WPD-TSNE-SVM模型故障诊断[J]. 机械科学与技术. 2022.
彭正伟，张维，张铃珠，等. 基于CNN+DAE集成模型的电机主轴轴承故障诊断研究[J]. 中国工程机械学报. 2023, 21(02): 166-171.
王伟平，王琦，于洋. 基于注意力机制与深度学习算法的机床主轴系统故障辨识[J]. 兵工学报. 2022, 43(4): 861-875.
张朝刚，侍中楼，李敏. 基于多状态时间序列预测学习的超精密机床主轴故障诊断仿真[J]. 吉林大学学报（工学版）. 2023, 53(11): 3056-3061.
Li J, Huang R, Xia J, et al. A Global-Local Dynamic Adversarial Network for Intelligent Fault Diagnosis of Spindle Bearing[C]. IEEE, 2021.
李滨，曾辉. 改进的深度置信网络在电主轴故障诊断中的应用[J]. 机械科学与技术. 2021, 40(7): 1051-1057.
王舒玮. 基于麻雀算法优化BP神经网络诊断数控机床故障[J]. 沈阳工业大学学报. 2023, 45(05): 546-551.
张洪，李开杰，王通德. 改进dynFWA优化BP神经网络在加工中心主轴故障诊断中的应用[J]. 噪声与振动控制. 2020, 40(3): 100-107.
余琨黎文献王日初马正青. 变形镁合金的研究、开发及应用[J]. 中国有色金属学报. 2003(02): 277-288.
Fang F, Tan W, Liu J Z. Tuning of coordinated controllers for boiler-turbine units[J]. Acta Automatica Sinica, 2005, 31(2): 291-296.
Fang F, Jizhen L, Wen T. Nonlinear internal model control for the boiler-turbine coordinate systems of power unit[J]. PROCEEDINGS-CHINESE SOCIETY OF ELECTRICAL ENGINEERING, 2004, 24(4): 195-199.
Zhang J, Feng J, Zhou Y, et al. Linear active disturbance rejection control of waste heat recovery systems with organic Rankine cycles[J]. Energies, 2012, 5(12): 5111-5125.
Liu J, Zeng D, Tian L, et al. Control strategy for operating flexibility of coal-fired power plants in alternate electrical power systems[J]. Proceedings of the CSEE, 2015, 35(21): 5385-5394.
Fang F, Zhu Z, Jin S, et al. Two-layer game theoretic microgrid capacity optimization considering uncertainty of renewable energy[J]. IEEE Systems Journal, 2020, 15(3): 4260-4271.
Lv Y, Fang F, Yang T, et al. An early fault detection method for induced draft fans based on MSET with informative memory matrix selection[J]. ISA transactions, 2020, 102: 325-334.
Fang F, Wu X. A win–win mode: The complementary and coexistence of 5G networks and edge computing[J]. IEEE Internet of Things Journal, 2020, 8(6): 3983-4003.
Lv Y, Lv X, Fang F, et al. Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants[J]. Energy, 2020, 192: 116589.
Fang F, Xiong Y. Event-driven-based water level control for nuclear steam generators[J]. IEEE Transactions on Industrial electronics, 2014, 61(10): 5480-5489.
Wang W, Liu J, Zeng D, et al. Modeling and flexible load control of combined heat and power units[J]. Applied Thermal Engineering, 2020, 166: 114624.
Wei L, Fang F. ${H} _ {infty} $-LQR-Based Coordinated Control for Large Coal-Fired Boiler–Turbine Generation Units[J]. IEEE Transactions on Industrial Electronics, 2016, 64(6): 5212-5221.
Zhang X, Fang F, Liu J. Weather-classification-MARS-based photovoltaic power forecasting for energy imbalance market[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8692-8702.
Liu Y, Fang F, Park J H, et al. Asynchronous output feedback dissipative control of Markovian jump systems with input time delay and quantized measurements[J]. Nonlinear Analysis: Hybrid Systems, 2019, 31: 109-122.
Hong F, Song J, Meng H, et al. A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images[J]. Solar Energy, 2022, 236: 406-416.
Liu Y, Fang F, Park J H. Decentralized dissipative filtering for delayed nonlinear interconnected systems based on T–S fuzzy model[J]. IEEE Transactions on Fuzzy Systems, 2018, 27(4): 790-801.
Liu Y, Park J H, Fang F. Global exponential stability of delayed neural networks based on a new integral inequality[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 49(11): 2318-2325.
Jin S, Wang S, Fang F. Game theoretical analysis on capacity configuration for microgrid based on multi-agent system[J]. International Journal of Electrical Power & Energy Systems, 2021, 125: 106485.
Cai W, Song Y, Duan H, et al. Multi-feature fusion-guided multiscale bidirectional attention networks for logistics pallet segmentation[J]. Computer Modeling in Engineering and Sciences, 2022, 131(3): 1539-1555.
Huang Z, Zhang P, Liu R, et al. An Improved YOLOv3-Based Method for Immature Apple Detection[J]. IECE Transactions on Internet of Things, 2023, 1(1): 9-14.
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