1.Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,
Kunming,650500
2.KSEC Intelligent Technology Co.,Ltd.,Kunming,650051
YIN Yanchao, SHI Chengjuan, ZOU Chaopu, LIU Xiaobao. Time-series Correlation Prediction of Quality in Process Production Processes Based on Deep TCN and Transfer Learning[J]. China Mechanical Engineering, 2023, 34(14): 1659-1671.
[1]梁强,张贤明,杜彦斌,等. 基于灰色关联分析的齿环热精锻成形工艺参数优化[J].计算机集成制造系统,2022,28(4):1020-1029.
LIANG Qiang,ZHANG Xianming,DU Yanbin,et al. Parameters Optimization in Hot Precision Forging Process of Synchronizer Ring Based on Grey Relational Analysis[J]. Computer Integrated Manufacturing Systems, 2022,28(4):1020-1029.
[2]胡嘉蕊,吕震宙.基于核主成分分析的多输出模型确认方法[J].北京航空航天大学学报,2017,43(7):1470-1480.
HU Jiarui,LYU Zhenzhou. Model Validation Method with Multivariate Output Based on Kernel Principal Component Analysis[J]. Journal of Beijing University of Aeronautics and Astronautics,2017,43(7):1470-1480.
[3]SIKDER S,MUKHERJEE I,PANJA S C. A Synergistic Mahalanobis-Taguchi System and Support Vector Regression Based Predictive Multivariate Manufacturing Process Quality Control Approach[J]. Journal of Manufacturing Systems,2020,57:323-337.
[4]TAHERI S,BRODIE G,GUPTA D. Optimised ANN and SVR Models for Online Prediction of Moisture Content and Temperature of Lentil Seeds in a Microwave Fluidized Bed Dryer[J]. Computers and Electronics in Agriculture,2021,182:106003.
[5]戴稳,张超勇,孟磊磊, 等.基于深度学习与特征后处理的支持向量机铣刀磨损预测模型[J].计算机集成制造系统,2020,26(9):2331-2343.
DAI Wen,ZHANG Chaoyong,MENG Leilei,et al. Support Vector Machine Milling Wear Prediction Model Based on Deep Learning and Feature Re-processing[J]. Computer Integrated Manufacturing Systems,2020,26(9):2331-2443.
[6]熊青春,王家序,周青华,等.融合机床精度与工艺参数的铣削误差预测模型[J].航空学报,2018,39(8):272-280.
XIONG Qingchun,WANG Jiaxu,ZHOU Qinghua,et al. Prediction Model of Machining Errors Based on Precision and Process Parameters of Machine Tools[J]. Acta Aeronautica et Astronautica Sinica,2018,39(8):272-282.
[7]雷凯云,秦训鹏,刘华明,等.基于神经网络的宽带激光熔覆熔池特征参数预测[J].光电子·激光,2018,29(11):1212-1220.
LEI Kaiyun,QIN Xunpeng,LIU Huaming,et al. Prediction on Characteristics of Molten Pool in Wide-band Laser Cladding Based on Neural Network[J]. Journal of Optoelectronics·Laser,2018,29(11):1212-1220.
[8]MOHANTY I,BANERJEE R,SANTARA A,et al. Prediction of Properties Over the Length of the Coil during Thermo-mechanical Processing Using DNN[J]. Ironmaking & Steelmaking,2021,48(8):953-961.
[9]方黄峰,刘瑶瑶,张文彪.基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测[J].化工学报,2020,71(20):307-314.
FANG Huangfeng,LIU Yaoyao,ZHANG Wenbiao. Biomass Moisture Content Prediction in Fluidized Bed Dryer Based on LSTM Neural Network[J]. CIESC Journal,2020,71(20):307-314.
[10]BAI Y,XIE J J,WANG D Q,et al.A Manufacturing Quality Prediction Model Based on AdaBoost-LSTM with Rough Knowledge[J]. Computers & Industrial Engineering,2021,155:107227.
[11]GU M,XU A,WANG H,et al. Real-time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM[J]. Processes,2021,9(11):1987.
[12]何彦,肖圳,李育锋,等.使用CNN-SVR的汽车组合仪表组装质量预测方法[J].中国机械工程,2022,33(7):825-833.
HE Yan,XIAO Zhen,LI Yufeng,et al. An Assembly Quality Prediction Method for Automotive Instrument Clusters Using CNN-SVR[J]. China Mechanical Engineering,2022,33(7):825-833.
[13]ZHANG X,LU X,LI W D,et al. Prediction of the Remaining Useful Life of Cutting Tool Using the Hurst Exponent and CNN-LSTM[J]. The International Journal of Advanced Manufacturing Technology,2021,112(7):2277-2299.
[14]QI J K,LUO N. Using Stacked Auto-encoder and Bi-directional LSTM for Batch Process Quality Prediction[J]. Journal of Chemical Engineering of Japan,2021,54(4):144-151.
[15]褚菲,彭闯,贾润达,等.基于多尺度核JYMKPLS迁移模型的间歇过程产品质量的在线预测方法[J].化工学报,2021, 72(4):2178-2189.
CHU Fei,PENG Chuang,JIA Runda,et al.Online Prediction Method of Batch Process Product Quality Based on Multi-scale Kernel JYMKPLS Transfer Model[J]. CIESC Journal,2021,72(4):2178-2189.
[16]LI J B,LU J,CHEN C Y,et al. Tool Wear State Prediction Based on Feature-based Transfer Learning[J]. The International Journal of Advanced Manufacturing Technology,2021,113(11):1-19.