Skip to main content

Advertisement

SpringerLink
Log in

Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Monitoring of machining processes is a critical requirement in the implementation of any unmanned operation in a shop floor and, particularly, in the establishment of Flexible Manufacturing Systems (FMS) and Computer Integrated Manufacturing (CIM) where most of the operations are carried out in an automated way. During the last years, notable efforts have been made to develop reliable and robust monitoring systems based on different types of sensors such as cutting force and torque, motor current and effective power, vibrations, acoustic emission or audible sound energy. This work is focused on this last sensor technology. The basic objective is to characterise the audible sound energy signals generated during different machining operations carried out on a milling machine. In order to achieve this, rotation speed, feed and depth of cut have been analysed separately. The main contributions of this work are, on the one hand, the application of a systematic methodology to set up the cutting tests and, on the other hand, the independent signal analysis of the noise generated by the milling machine used for the cutting tests in order to filter this noise out from the signals obtained during the actual material processing. The classification of audible sound signal features for process monitoring has been obtained by graphical analysis and parallel distributed data processing using a supervised neural network (NN) paradigm.

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

Access this article

Log in via an institution

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

  • Brophy C., Kelly K. and Byrne G. (2002). AI-based condition monitoring of the drilling process. Journal of Materials Processing Technology 124(3): 305–310

    Article  Google Scholar 

  • Burke L.I. and Rangwala S. (1991). Tool condition monitoring in metal cutting. A neural network approach. Journal of Intelligent Manufacturing 2(5): 269–280

    Google Scholar 

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

    Article  Google Scholar 

  • CA-Cricket Graph III for Macintosh: Version 1.0 User Guide, 1992.

  • Chen J.C. (2000). An effective fuzzy-nets training scheme for monitoring tool breakage. Journal of Intelligent Manufacturing 11(1): 85–101

    Article  Google Scholar 

  • Chen J.C. and Chen W.L. (1999). A tool breakage detection system using an accelerometer sensor. Journal of Intelligent Manufacturing 10(2): 187–197

    Article  Google Scholar 

  • Chen C., Lee S. and Santamarina G. (1994). An object-oriented manufacturing control system. Journal of Intelligent Manufacturing 5(5): 315–321

    Article  Google Scholar 

  • Chen F.F., Huang J. and Centeno M.A. (1999). Intelligent scheduling and control of rail-guided vehicles and load/unload operations in a flexible manufacturing system. Journal of Intelligent Manufacturing 10(5): 405–421

    Article  Google Scholar 

  • Cho D.W., Lee S.J. and Chu C.N. (1999). The state of machining process monitoring research in Korea. International Journal of Machine Tools and Manufacturing 39(11): 1697–1715

    Article  Google Scholar 

  • Clark W.I., Shih A.J., Hardin C.W., Lemaster R.L. and McSpadden S.B. (2003). Fixed abrasive diamond wire machining—part I: process monitoring and wire tension force. International Journal of Machine Tools Manufacturing 43(5): 523–532

    Article  Google Scholar 

  • D’Errico G.E. (1997). Adaptive systems for machining process monitoring and control. Journal of Materials Processing Technology 64(1–3): 75–84

    Article  Google Scholar 

  • Desforges X., Habbadi A., Geneste L. and Soler F. (2004). Distributed machining control and monitoring using smart sensors/actuators. Journal of Intelligent Manufacturing 15(1): 39–53

    Article  Google Scholar 

  • Dornfeld D.A. (1992). Monitoring of machining process—literature review. CIRP Annals 41(1): 93–96

    Article  Google Scholar 

  • Emel E. (1991). Tool wear detection by neural network based acoustic emission sensing. ASME, Dynamic Systems and Control Division Publication 28: 79–85

    Google Scholar 

  • Fu J.C., Troy C.A. and Mori K. (1996). Chatter classification by entropy functions and morphological processing in cylindrical traverse grinding. Precision Engineering 18(2–3): 110–117

    Article  Google Scholar 

  • Govekar E., Gradišek J. and Grabec I. (2000). Analysis of acoustic emission signals and monitoring of machining processes. Ultrasonics 38(1–8): 598–603

    Article  Google Scholar 

  • Grabec E., Govekar E. and Susic B. (1998). Antolovic, Monitoring manufacturing processes by utilizing empirical modeling. Ultrasonics 36(1–5): 263–271

    Article  Google Scholar 

  • Hong S.Y. (1993). Knowledge-based diagnosis of drill conditions. Journal of Intelligent Manufacturing 4: 233–241

    Article  Google Scholar 

  • Hou T.H., Liu W.L. and Lin L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing 14(2): 239–253

    Article  Google Scholar 

  • Huang P.T. and Chen J.C. (1998). Fuzzy logic-base tool breakage detecting system in end milling operations. Computers & Industrial Engineering 35(1–2): 37–40

    Article  Google Scholar 

  • Inasaki I. (1998). Application of acoustic emission sensor for monitoring machining processes. Ultrasonics 36(1-5): 273–281

    Article  Google Scholar 

  • Jemielniak K., Kwiatkowski L. and Wrzosek P. (1998). Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. Journal of Intelligent Manufacturing 9(5): 447–455

    Article  Google Scholar 

  • Jin J. and Shi J. (2001). Automatic feature extraction of waveform signals for in-process diagnostic performance improvement. Journal of Intelligent Manufacturing 12(3): 257–268

    Article  Google Scholar 

  • Karlsson B., Karlsson N. and Wide P. (2000). A dynamic safety system based on sensor fusion. Journal of Intelligent Manufacturing 11(5): 475–483

    Article  Google Scholar 

  • Kopac J. and Sali S. (2001). Tool wear monitoring during the turning process. Journal of Materials Processing Technology 113: 312–316

    Article  Google Scholar 

  • Larson Davis Laboratory, 2800 Manual, Preliminary documentation 1/27/93.

  • Lin B., Zhu M.Z., Yu S.Y., Zhu H.T. and Lin M.X. (2002). Study of synthesis identification in the cutting process with a fuzzy neural network. Journal of Materials Processing Technology 129(1–3): 131–134

    Article  Google Scholar 

  • Lu, M. C., & Kannatey-Asibu, E. Jr. (2000). Analysis of sound signal generation due to flank wear in turning. Orlando, FL: International ME2000 Congress & Exposition.

  • Malakooti B.B., Zhou Y.Q. and Tandler E.C. (1995). In-process regressions and adaptive multicriteria neural networks for monitoring and supervising machining operations. Journal of Intelligent Manufacturing 6(1): 53–66

    Article  Google Scholar 

  • Masory O. (1991). Monitoring machining processes using multi-sensor readings fused by artificial neural network. Journal of Materials Processing Technology 28(1-2): 231–240

    Article  Google Scholar 

  • Masters T. (1993). Practical neural networks recipies in C++. Academic press, San Diego, CA

    Google Scholar 

  • Niu Y., Wong Y. and Hong G. (1998). Intelligent sensor system approach for reliable tool flank wear recognition. International Journal of Advanced Manufacturing Technology 14(2): 77–84

    Article  Google Scholar 

  • Noise and Vibrations Works, References Manual version 1.22.

  • Okafor C. and Adetona O. (1995). Predicting quality characteristics of end-milled parts based on multi-sensor integration using neural networks, individual effects of learning parameters and rules. Journal of Intelligent Manufacturing 6(6): 389–400

    Article  Google Scholar 

  • Ouafi A.E., Guillot M. and Bedrouni A. (2000). Accuracy enhancement of multi-axis CNC machines through on-line neurocompensation. Journal of Intelligent Manufacturing 11(6): 535–545

    Article  Google Scholar 

  • Peng Y. (2004). Intelligent condition monitoring using fuzzy inductive learning. Journal of Intelligent Manufacturing 15(3): 373–380

    Article  Google Scholar 

  • Rubio, E. M., & Teti, R. (2005). Cutting tests definition for effective tool condition monitoring, International Federation of Automatic Control. IFAC-MIM’04. IFAC Conference on Manufacturing, Modelling, Management and Control, S.A., Amsterdam: Elsevier Science.

  • Rubio, E. M., Teti, R., & Baciu, I. L. (2006a). Advanced signal processing used in monitoring systems of machining processes based on acoustic emission sensors, 2st I*PROMS Virtual International Conference on Intelligent Production Machines and Systems, S.A., Amsterdam: Elsevier Science.

  • Rubio E.M., Teti R. and Baciu I.L. (2006b). Main decision making procedures used in the monitoring systems of machining processes based on acoustic emission sensors. Intelligent Computation in Manufacturing Engineering 5: 189–192

    Google Scholar 

  • Shawaky A., Rosenberger T. and Elbestawi M. (1998). In process monitoring and control of thickness error in machining hollows shafts. Mechatronics 8: 301–322

    Article  Google Scholar 

  • Sokolowski A. and Kosmol J. (2001). Selected examples of cutting process monitoring and diagnostics. Journal of Materials Processing Technology 113(1–3): 322–330

    Article  Google Scholar 

  • Spectrum Pressure Lavel (Spl 3100), Manual Version 0.98, Program Version 1.10.

  • Teti R. (1995). A Review of Tool Condition Monitoring a Literature. CIRP Annals 44(2): 659–666

    Google Scholar 

  • Teti R. and Baciu I.L. (2004). Neural network processing of audible sound parameters for sensor monitoring of tool conditions. Intelligent Computation in Manufacturing Engineering 4: 385–390

    Google Scholar 

  • Teti R. and Buonadonna P. (1999). Round Robin on Acoustic Emission Monitoring of Machining. Annals of the CIRP 48: 47–69

    Article  Google Scholar 

  • Teti, R., Baciu, I. L., & Rubio, E. M. (2004). Neural network classification of audible sound signals for process monitoring during machining. Proceedings of the 15th International DAAAM SYMPOSIUM: “Intelligent Manufacturing & Automation: Globalisation—Technology—Men—Nature”, Viena.

  • Tönshoff H.K., Wulsferg J.P., Kals H.J., Köning W. and Van Luttervelt C.A. (1988). Developments and trends in monitoring and control of machining processes. CIRP Annals 37(2): 611–622

    Article  Google Scholar 

  • Venkatesh K., Zhou M. and Caudill R.J. (1997). Design of artificial neural networks for tool wear monitoring. Journal of Intelligent Manufacturing 8(3): 215–226

    Article  Google Scholar 

  • Wilcos S.J., Reuben R.L. and Souquet P. (1997). The use of cutting force and acoustic emission signals for the monitoring the tool insert geometry during rough face milling. International Journal of Machine Tools Manufacturing 32(4): 481–494

    Article  Google Scholar 

  • Xiaoli L., Yingxue Y. and Zhejun Y. (1997). On-line tool condition monitoring system with wavelet fuzzy neural network. Journal of Intelligent Manufacturing 8(4): 271–276

    Article  Google Scholar 

  • Xu Y. and Ge M. (2004). Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing 15(3): 337–350

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Manufacturing Engineering, National Distance University of Spain (UNED), Juan del Rosal 12, Madrid, Spain

    E. M. Rubio

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

    R. Teti

Authors
  1. E. M. Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Teti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. M. Rubio.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rubio, E.M., Teti, R. Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound. J Intell Manuf 20, 43–54 (2009). https://doi.org/10.1007/s10845-008-0102-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-008-0102-8

Keywords

Search

Navigation