[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Signal processing and pattern recognition for surface roughness assessment in multiple sensor monitoring of robot-assisted polishing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Polishing processes have steadily evolved from largely manual operations to automated processes based on robotized systems. Sensor monitoring can be a viable solution for process control in order to achieve more accurate, robust, and reliable automated polishing operations. In this paper, an acoustic emission-, strain-, and current-based sensor-monitoring system was employed during robot-assisted polishing of steel bars for online assessment of workpiece surface roughness. Two feature extraction procedures, a conventional one based on statistics and an advanced one based on wavelet packet transform, were applied to the sensor signals detected during polishing. The extracted relevant features were utilized to construct different types of pattern feature vectors (basic and sensor fusion pattern vectors) to be fed to a neural network pattern recognition paradigm in order to make a decision on polished part surface roughness-level acceptability.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Evans CJ, Paul E, Dornfeld D, Lucca DA, Byrne G, Tricard M, Klocke F, Dambon O, Mullany BA (2003) Material removal mechanisms in lapping and polishing. CIRP Ann 52(2):611–633

    Article  Google Scholar 

  2. Marinescu I, Uhlmann E, Doi T (2006) Handbook of lapping and polishing. CRC Press, Taylor & Francis Group

  3. Komanduri R, Lucca DA, Tani Y (1997) Technological advances in fine abrasive processes. CIRP Ann 46(2):545–596

    Article  Google Scholar 

  4. Rebeggiani S, Rosén B G (2011) High gloss polishing of tool steels—step by step. Proc. of the 4th Swedish Production Symposium, Lund, Sweden. The Swedish Production Academy, pp 257–262

  5. Eriksen RS, Arentoft M, Grønbæk J, Bay N (2012) Manufacture of functional surfaces through combined application of tool manufacturing processes and robot assisted polishing. CIRP Ann 61(1):563–566

    Article  Google Scholar 

  6. Klocke F, Dambon O, Behrens B (2011) Analysis of defect mechanisms in polishing of tool steels. Production Engineering—Research and Development 5(5):475–483

    Article  Google Scholar 

  7. Byrne G, Dornfeld D, Inasaki I, König W, Teti R (1995) Tool condition monitoring (TCM)—the status of research and industrial application. CIRP Ann 44(2):541–567

    Article  Google Scholar 

  8. Wang C, Ghani SBC, Cheng K, Rawkoski R (2013) Adaptive smart machining based on using constant cutting force and a smart cutting tool. Proc of the IMechE, Part B: Journal of Engineering Manufacture 227(2):249–253

    Article  Google Scholar 

  9. Wang C, Cheng K, Chen X, Minton T, Rawkoski R (2014) Design of an instrumented smart cutting tool and its implementation and application perspectives. Smart Mater Struct 23(3):350–362

    Article  Google Scholar 

  10. Jemielniak K, Teti R, Kossakowska J, Segreto T (2006) Innovative signal processing for cutting force based chip form prediction. In: Intelligent Production Machines and Systems—2nd I*PROMS Virtual International Conference, pp 7–12

  11. Micheletti GF, Koenig W, Victor HR (1976) In process tool wear sensors for cutting operations. CIRP Ann 25(2):483–496

    Google Scholar 

  12. Toenshoff HK, Wulfsberg JP, Kals HJJ, van Luttervelt CA (1988) Development and trends in monitoring and control of machining processes. CIRP Ann 37(2):611–622

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Klein LA (2004) Sensor and data fusion: a tool for information assessment and decision making. SPIE Press Book

  15. Hall D, Llinas J (2001) Handbook of multisensor data fusion. CRC Press

  16. White FE (1991) Data fusion lexicon, joint directors of laboratories, technical panel for C3, data fusion sub-panel. Naval Ocean Systems Center, San Diego

    Google Scholar 

  17. Ghosh N, Ravi A, Patra S, Mukhopadhyay S, Mohanty A, Chattopadhyay A (2007) Estimation of tool wear during CNC milling using NN-based sensor fusion. Mech Syst & Sign Proc 21:466–479

    Article  Google Scholar 

  18. Chen L, Bender P, Renton P, El-Wardany T (2002) Integrated virtual manufacturing systems for process optimisation and monitoring. CIRP Ann 51(1):409–412

    Article  Google Scholar 

  19. Huang B, Chen JC (2003) An in-process neural network-based surface roughness prediction system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21:339–347

    Article  Google Scholar 

  20. Binsaeid S, Asfoura S, Chob S, Onarc A (2009) Machine ensemble approach for simultaneous detection of transient and gradual anomalies in milling using multisensor fusion. J. Mat Proc Techn 209:4728–4738

    Article  Google Scholar 

  21. Ahn JH, Shen YF, Kim HY, Jeong HD, Cho KK (2001) Development of a sensor information integrated expert system for optimizing die polishing. Robot Comput Integr Manuf 17:269–276

    Article  Google Scholar 

  22. Ahn JH, Lee MC, Jeong HD, Kim SR, Cho KK (2002) Intelligently automated polishing for high quality surface formation of sculptured die. J Mater Process Technol 130-131:339–344

    Article  Google Scholar 

  23. Lee DE, Hwang I, Valente CMO, Oliveira JFG, Dornfeld DA (2006) Precision manufacturing process monitoring with acoustic emission. Int J Adv Manuf Technol 46:176–188

    Google Scholar 

  24. Chang YP, Hashimura M, Dornfeld DA (1996) An investigation of the AE signals in the lapping process. CIRP Ann 45:331–334

    Article  Google Scholar 

  25. Dunegan H (1998) An acoustic emission technique for measuring surface roughness. The DECI report. Dunegan Engineering Consultants Inc., Midland, Texas 79711

  26. Segreto T, Karam S, Teti R, Ramsing J (2015) Cognitive decision making in multiple sensor monitoring of robot assisted polishing. Procedia CIRP 33:333–338

    Article  Google Scholar 

  27. Karam S, Teti R (2013) Wavelet transform feature extraction for chip form recognition during carbon steel turning. Procedia CIRP 12:97–102

    Article  Google Scholar 

  28. Segreto T, Karam S, Simeone A, Teti R (2013) Residual stress assessment in Inconel 718 machining through wavelet sensor signal analysis and sensor fusion pattern recognition. Procedia CIRP 9:103–108

    Article  Google Scholar 

  29. Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    MATH  Google Scholar 

  30. Segreto T, Simeone A, Teti R (2014) Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning. CIRP J of Manuf Sci and Techn 7:202–209

    Article  Google Scholar 

  31. Segreto T, Karam S, Teti R, Ramsing J (2015) Feature extraction and pattern recognition in acoustic emission monitoring of robot assisted polishing. Procedia CIRP 28:22–27

    Article  Google Scholar 

  32. EC FP7, call for FoF, towards zero-defect manufacturing, 2011-NMT-ICT-FoF, intelligent fault correction and self optimising manufacturing systems—IFaCOM (2011–15)

  33. (2011) Exploring surface texture: a fundamental guide to the measurement of surface finish, 7th ed. Taylor Hobson, UK

  34. Beattie AG (1979) Studies in the digital analysis of acoustic emission signals. In: Kanji Ono (ed) Fundamentals of acoustic emission. University of California LA, pp 17–47

  35. Webster J, Dong WP, Lindsay R (1996) Raw acoustic emission signal analysis of grinding process. CIRP Ann 45(1):335–340

    Article  Google Scholar 

  36. Misiti M, Misiti Y, Oppenheim G, Poggi JM (1997-2014) Wavelet toolbox for use with MATLAB. The MathWorks, Inc

  37. Segreto T, Simeone A, Teti R (2013) Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion. Procedia CIRP 12:85–90

    Article  Google Scholar 

  38. Segreto T, Simeone A, Teti R (2012) Sensor fusion for tool state classification in nickel superalloy high performance cutting. Procedia CIRP 1:593–598

    Article  Google Scholar 

  39. Jordan MI, Bishop CM (2004) Neural networks. In: Allen B. Tucker (edn) Computer science handbook, 2nd edn. (Section VII: Intelligent Systems), Chapman & Hall/CRC Press LLC

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT Naples), Naples, Italy

    Tiziana Segreto, Sara Karam & Roberto Teti

  2. Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125, Naples, Italy

    Tiziana Segreto, Sara Karam & Roberto Teti

Authors
  1. Tiziana Segreto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara Karam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberto Teti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tiziana Segreto.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Segreto, T., Karam, S. & Teti, R. Signal processing and pattern recognition for surface roughness assessment in multiple sensor monitoring of robot-assisted polishing. Int J Adv Manuf Technol 90, 1023–1033 (2017). https://doi.org/10.1007/s00170-016-9463-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9463-x

Keywords