Skip to main content
SpringerLink
Log in

Mathematical Foundations of Machining System Monitoring

  • Chapter
  • First Online:
Smart Machining Systems

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

  • 850 Accesses

Abstract

To ensure the safety and processing quality of high investment automation processing equipment, machining process monitoring is becoming an urgent problem to be solved in the modern machining system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Teti R, Jemielniak K, O’Donnel G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Techn 59(2):717–739

    Article  Google Scholar 

  2. Zhou Z, Chen Y (1999) The monitoring and fault diagnosis of modern manufacturing systems. Huazhong University of Science and Technology Press

    Google Scholar 

  3. Grzesik W (2017) Advanced machining processes of metallic materials: theory, modelling and applications, 2nd edn. Elsevier

    Google Scholar 

  4. Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58(2):588–607

    Article  Google Scholar 

  5. Ljung L (1999) System identification: theory for the user. Prentice-Hall

    Google Scholar 

  6. Bendat JS (2010) Random data analysis and measurement procedures. Wiley

    Google Scholar 

  7. Manolakis DG, Ingle VK, Kogon SM (2000) Statistical and adaptive signal processing. McGraw-Hill Education

    Google Scholar 

  8. Shumway RH, Stoffer DS (2017) Time series analysis and its application, 4th edn. Springer

    Google Scholar 

  9. Altintas Y, Yellowley I (1989) The process detection of tool failure in milling using cutting force models. ASME J Eng Ind 111:149–157

    Article  Google Scholar 

  10. Kumar SA, Ravindra HV, Srinivasa YG (1997) In-process tool wear monitoring through time series modeling and pattern recognition. Int J Prod Res 35(3):739–751

    Article  Google Scholar 

  11. Gradisek J, Govekar E, Grabec I (1998) Time series analysis in metal cutting: chatter versus chatter-free cutting. Mech Syst Signal Process 12(6):839–854

    Article  Google Scholar 

  12. Tönshoff HK (ed) (2001) Sensors in manufacturing, vol 1. Wiley-VCH

    Google Scholar 

  13. Snr D (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40(8):1073–1098

    Article  Google Scholar 

  14. Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96:2509–2523

    Article  Google Scholar 

  15. Kuntoglu M, Saglam H (2020) Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Measurement 108582

    Google Scholar 

  16. Özel T, Nadgir A (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int J Mach Tools Manuf 42:287–297

    Article  Google Scholar 

  17. Bhattacharyya P, Sengupta D, Mukhopadhyay S (2007) Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech Syst Signal Process 21(6):2665–2683

    Article  Google Scholar 

  18. Brinksmeier E, Preuss W, Riemer O, Rentsch R (2017) Cutting forces, tool wear and surface finish in high speed diamond machining. Precis Eng 49:293–304

    Article  Google Scholar 

  19. Zhu KP, Zhang Y (2019) A generic tool wear model and its application to force modeling and wear monitoring in high speed milling. Mech Syst Signal Process 115(15):147–161

    Article  Google Scholar 

  20. Sevilla P, Robles J, Muñiz J, Lee F (2015) Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. Int J Adv Manuf Technol 81(5–8):1–8

    Google Scholar 

  21. Zhou Y, Liu X, Li F, Sun B, Xue W (2015) An online damage identification approach for numerical control machine tools based on data fusion using vibration signals. J Vib Control 21(15):2925–2936

    Article  Google Scholar 

  22. Aghdam B, Vahdati M, Sadeghi M (2015) Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int J Adv Manuf Technol 76:1631–1642

    Article  Google Scholar 

  23. Dimla DE (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19:705–713

    Article  Google Scholar 

  24. Kataoka R, Shamoto E (2019) Influence of vibration in cutting on tool flank wear: Fundamental study by conducting a cutting experiment with forced vibration in the depth-of-cut direction. Precis Eng 55:322–329

    Article  Google Scholar 

  25. Bhuiyan M, Choudhury IA, Dahari M, Nukman Y, Dawal S (2016) Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement 92:208–217

    Article  Google Scholar 

  26. Chiou RY, Liang SY (2000) Analysis of acoustic emission in chatter vibration with tool wear effect in turning. Int J Mach Tools Manuf 40:927–941

    Article  Google Scholar 

  27. Maia LHA, Abrao AM, Vasconcelos WL, Sales WF, Machado AR (2015) A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission. Tribol Int 92:519–532

    Article  Google Scholar 

  28. Wang C, Bao Z, Zhang P, Ming W, Chen M (2019) Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals. Measurement 138:256–265

    Article  Google Scholar 

  29. Jemielniak K, Arrazola P (2008) application of AE and cutting force signals in tool conditionmonitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102

    Article  Google Scholar 

  30. Pechenin V, Khaimovich A, Kondratiev A, Bolotov M (2017) Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling. Procedia Eng 176:246–252

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Precision Manufacturing Laboratory, Institute of Advanced Manufacturing Technology, Chinese Academy of Sciences, Changzhou, Jiangsu, China

    Kunpeng Zhu

  2. Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, Hubei, China

    Kunpeng Zhu

Authors
  1. Kunpeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunpeng Zhu .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhu, K. (2022). Mathematical Foundations of Machining System Monitoring. In: Smart Machining Systems. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-87878-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87878-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87877-1

  • Online ISBN: 978-3-030-87878-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation