[1]郑小虎,鲍劲松,马清文,等.基于模拟退火遗传算法的纺纱车间调度系统[J].纺织学报,2020,41(06):36-41. [2]Yang Qirui, Xu Kaizhou, Zheng Xiaohu, et al. Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)[J]. Journal of Donghua University(English Edition). 2019,36(04):364-368. [3]郑小虎,张洁.数字孪生技术在纺织智能工厂中的应用探索[J].纺织导报,2019(03):37-41. [4]袁广超,鲍劲松,郑小虎,等.基于CNC实时监测数据驱动方法的钛合金高速铣削刀具寿命预测[J].中国机械工程,2018,29(04):457-462+470. [5] Jinsong B, Yuan G, Xiaohu Z*. A Data Driven Model for Predicting Tool Health Condition in High Speed Milling of Titanium Plates Using Real-Time SCADA[J]. Procedia CIRP, 2017, 61: 317-322. [6] Zheng X*, Dong D, Huang L, et al. Research on fixture hole drilling quality of printed circuit board[J]. International Journal of Precision Engineering and Manufacturing, 2013, 14(4): 525-534. [7] Zheng X*, Liu Z, An Q, et al. Experimental investigation of microdrilling of printed circuit board[J]. Circuit World, 2013, 39(2): 82-94. [8] Zheng X*, Liu Z, Chen M, et al. Experimental study on micro-milling of Ti6Al4V with minimum quantity lubrication[J]. International Journal of Nanomanufacturing, 2013, 9(5-6): 570-582. |
