朱錕鵬

朱錕鵬

朱錕鵬，男，博士，中科院合肥物質科學研究院研究員、博士生導師，精密製造實驗室主任，洪堡學者。

基本介紹

人物經歷,研究領域,學術職務,科研項目,科研成果,代表專著,代表論文,

人物經歷

1994/9月-1998/7月 武漢科技大學機械工程學士
1999/9月-2002/7月 華中科技大學動力工程碩士
2003/1月-2007/7月 新加坡國立大學機械工程系博士
2007/8月-2011/6月 新加坡國立大學機械工程系博士後
2011/7月-2013/9月 德國慕尼黑工業大學自動化與信息系統研究所洪堡學者
2013/3月-2013/6月 英國克蘭菲爾德大學振動與聲學研究中心訪問學者
2013/11月起 中國科學院合肥物質科學研究院先進制造技術研究所研究員 （中科院“百人計畫”）

研究領域

  1. 精密製造理論及自動化
  2. 金屬增材製造工藝及智慧型控制
  3. 航空航天裝備製造與智慧型工廠
  4. 智慧型製造技術與系統

學術職務

  1. IEEE/ASME Transactions on Mechatronics，副編輯/Technical Editor
  2. IEEE Transactions on Automation Science and Engineering，副編輯/Associate Editor
  3. ISA Transactions，副編輯/Associate Editor
  4. Computers in Industry，編委/Editorial Board
  5. Computer-Aided and Digital Manufacturing Technologies，副編輯（助理主編）/Associate Editor

科研項目

  1. 國家重點研發計畫“多品種小批量航天複雜構件加工智慧型工廠集成技術研究和套用示範”，2019-2022，科技部；項目負責人
  2. 中科院百人計畫（A類）擇優項目，精密微細銑削加工機理及過程建模與智慧型控制，2014-2017，中國科學院；項目負責人
  3. 自然科學基金面上項目，基於壓縮感測的微銑削加工刀具磨破損線上監測與壽命預測，2015.01-2018.12，國家自然科學基金；項目負責人

科研成果

在國際知名期刊 IEEE Trans on Industrial Informatics, InternationalJournal of Machine Tool & Manufacture等發表SCI論文70餘篇，出版英文專著2部，論文引用2600餘次。

代表專著

  1. Zhu K.P., Smartmachining system: modelling, monitoring and informatics, Springer, 2022.
  2. 朱錕鵬, 傅盈西,雷射增材製造線上監控系統理論與技術，國防工業出版社，2021.

代表論文

  1. Zhu K.P., Guo H., Li S.,Lin X., Physics-informed deep learning for tool wear monitoring, IEEETransactions on Industrial Informatics, 2023, 20(1): 524-533.
  2. Wang Q., Mao Y., Zhu K.P., Melt Pool Size Prediction of Laser Powder Bed Fusion by Process and ImageFeature Fusion, IEEE Transactions On Instrumentation and Measurement, 2023, 73: 2503212.
  3. Zhu K.P., Huang C., Li S., Lin X., Physics-informed Gaussian Process for Tool Wear Prediction, ISA Transactions, 2023, 143: 548-556.
  4. Zhu K.P., Guo H., Li S., Lin X., Online tool wear monitoring by super-resolution based machine vision, Computers in Industry, 2022, 144: 103782.
  5. Zhu K.P., Fuh J.Y.H., Lin X., Metal-based AdditiveManufacturing Condition Monitoring: A Review on Machine Learning BasedApproaches, IEEE/ASME Transactions on Mechatronics, 2022, 27(5): 2495-2510.
  6. Guo H., Zhang Y., Zhu K.P., Interpretable deep learning approach for tool wearmonitoring in high-speed milling, Computers in Industry, 138(6):103638, 2022.
  7. Liu T., Zhu K.P., A Switching Hidden Semi-Markov Model for DegradationProcess and Its Application to Time-Varying Tool Wear Monitoring, IEEETransactions on Industrial Informatics, 2021. 17(4): 2621-2631.
  8. Zhang Y., Hong G. S., Ye D., Jerry Y. H.Fuh, Zhu K.P., Powder-bed fusionprocess monitoring by machine vision with hybrid convolutional neural networks,IEEE Transactions on Industrial Informatics, 2020, 16(9):5769-5779.
  9. Zhu K.P., Li G.C., Zhang Y., Big Data Oriented Smart ToolCondition Monitoring System, IEEE Transactions on Industrial Informatics,2020,16( 6): 4007-4016.
  10. Zhu K.P., Lin X., Tool Condition Monitoring withMultiscale Discriminant Sparse Decomposition, IEEE Transactions on IndustrialInformatics, 2019, 15(5): 2819-2827.
  11. Zhu K.P., Zhang Y. A generic tool wear model and itsapplication to force modeling and wear monitoring in high speed milling,Mechanical Systems and Signal Processing, 2019, 115(15):147-161.
  12. Zhu K.P., Zhang Y., A Cyber-Physical Production SystemFramework of Smart CNC Machining Monitoring System, IEEE/ASME Transactions on Mechatronics,2018, 23(6):2579-2586.
  13. Liu T. S., Zhu K.P., Zeng L.C., Diagnosis and Prognosis for DegradationProcess via Hidden Semi-Markov Model, IEEE/ASME Transactions on Mechatronics, 2018,23(3):1456-1466.
  14. Lin X., Zhu K.P., Wang Q-G., Anisotropic Diffusion Map Based SpectralEmbedding for 3D CAD Model Retrieval, IEEE Transactions on IndustrialInformatics: 2018,14(1): 265-274.
  15. Ye D, Fuh Y H J, Zhang Y, Hong G, Zhu KP. In situ monitoring of selectivelaser melting using plume and spatter signatures by deep belief networks, ISATransactions, 2018, 81:96-104.
  16. Zhu K.P., Liu T., On-line High-Speed Milling Tool Wear Monitoring via Hidden Semi-Markov Model with Dependent Durations, IEEETransactions on Industrial Informatics: 2018,14(1): 69-78.
  17. Dai Y., Zhu K.P., A machine vision system for micro-milling tool conditionmonitoring, Precision Engineering. 2018, 52:183-191.
  18. Zhu K.P., Zhang Y., Modeling of the InstantaneousMilling Force per Tooth in High Speed Ball-end Milling with Tool Run-out Effect, International Journal of Machine Tool & Manufacture: 2017,(118-119):37-48.
  19. Zhu K.P., Yu X.L, The monitoring of micro milling toolwear conditions by wear area estimation, Mechanical System and SignalProcessing: 2017,38:80-91.
  20. Li K., Zhu K.P., Mei T., A generic instantaneous undeformed chip thicknessmodel for the cutting force modeling in micro-milling, International Journal ofMachine Tools & Manufacture: 2016,105(2016): 23-31.
  21. Zhu K.P., Vogel B.H., Sparse decomposition in the time-frequency domain and its application to micro-milling, InternationalJournal of Advanced Manufacturing: 2014, 68(2014):1-17.
  22. Zhu K.P., Hong G.S., Wong Y.S., Multi-Scale SingularityAnalysis of Cutting Forces for Micro-Milling Tool Wear Monitoring, IEEETransactions on Industrial Electronics: 2011, 58(2):2512-2521.
  23. Zhu K.P., Wong Y.S., Hong G.S., Wavelet Analysis ofSensor Signals for Tool Condition Monitoring: some new results, InternationalJournal of Machine Tools & Manufacture,2009 (4):537-553.
  24. Zhu K.P., Wong Y.S., Hong G.S., Multi-category Micro-milling Tool Wear Classification with Continuous Hidden Markov Models, Mechanical System andSignal Processing, 2009 (23): 547-560.

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