Advertisement

Machining Process Monitoring

  • Huaizhong LiEmail author
  • Yun Chen
Reference work entry

Abstract

In the modern manufacturing industry, monitoring the machining process and tool condition is becoming increasingly important in order to achieve better product quality, higher productivity, higher process automation, and lower human labor costs. This chapter introduces the fundamental technologies and state-of-the-art development for machining process monitoring. After a brief introduction of the background, in section “Measurands and Sensors,” the commonly used measurands for machining process monitoring are presented, including motor power and current, force, torque, acoustic emission, vibration, image, temperature, displacement, strain, etc. The corresponding sensors for these measurands, and the requirement for signal conditioning, are also discussed. Signal conditioning includes amplification, filtering, converting, range matching, isolation, and other processes to make sensor output suitable for data acquisition and signal processing. Knowledge of data acquisition, which is the process of sampling sensor signals and converts the resulting samples into digital numeric values that can be manipulated by a computer, is provided in section “Data Acquisition.” Some key concepts such as analog-to-digital conversion, quantization, sampling rate, Nyquist sampling theorem, and aliasing are explained. Section “Signal Processing” introduces the essential signal process techniques, including the time domain analysis, frequency domain analysis, time-frequency domain analysis, and artificial intelligence approaches such as artificial neural networks, fuzzy logic, etc. Detailed machining process monitoring strategies and approaches, together with some examples and case studies, are provided in section “Monitoring Strategies and Approaches,” which covers the topics of tool wear estimation, tool breakage detection, chatter detection, surface integrity, and chip monitoring.

Keywords

Machine Tool Acoustic Emission Root Mean Square Tool Wear Support Vector Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Abburi NR, Dixit US (2006) A knowledge-based system for the prediction of surface roughness in turning process. Robot Comp Integr Manuf 22(4):363–372CrossRefGoogle Scholar
  2. Abellan-Nebot J, Romero Subirón F (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257CrossRefGoogle Scholar
  3. Achiche S, Balazinski M, Baron L, Jemielniak K (2002) Tool wear monitoring using genetically-generated fuzzy knowledge bases. Eng Appl Artif Intel 15(3–4):303–314CrossRefGoogle Scholar
  4. Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge University Press, Cambridge/New YorkGoogle Scholar
  5. Andreasen JL, De Chiffre L (1998) An automatic system for elaboration of chip breaking diagrams. CIRP Ann Manuf Technol 47(1):35–40CrossRefGoogle Scholar
  6. Arrazola PJ, Arriola I, Davies MA, Cooke AL, Dutterer BS (2008) The effect of machinability on thermal fields in orthogonal cutting of AISI 4140 steel. CIRP Ann Manuf Technol 57(1):65–68CrossRefGoogle Scholar
  7. Axinte DA, Gindy N (2003) Tool condition monitoring in broaching. Wear 254(3–4):370–382CrossRefGoogle Scholar
  8. Axinte DA, Gindy N, Fox K, Unanue I (2004) Process monitoring to assist the workpiece surface quality in machining. Int J Mach Tool Manuf 44(10):1091–1108CrossRefGoogle Scholar
  9. Byrne G, Dornfeld D, Inasaki I, Konig W, Teti R (1995) Tool condition monitoring (TCM) – the status of research and industrial application. CIRP Ann Manuf Technol 44(2):541–567CrossRefGoogle Scholar
  10. Chen JC, Huang B (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol 21(5):339–347CrossRefGoogle Scholar
  11. Chen XQ, Zeng H, Li HZ (2008) In-process sensing and monitoring for intelligent machining: overview and implementation. Int J Process Syst Eng (IJPSE) 1:1–12Google Scholar
  12. Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection using support vector machine learning in a milling process. Int J Mach Tool Manuf 45(3):241–249CrossRefGoogle Scholar
  13. Chungchoo C, Saini D (2002) On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int J Mach Tool Manuf 42(1):29–40CrossRefGoogle Scholar
  14. Çolak O, Kurbanoğlu C, Kayacan MC (2007) Milling surface roughness prediction using evolutionary programming methods. Mater Design 28(2):657–666CrossRefGoogle Scholar
  15. Davies MA, Ueda T, M’Saoubi R, Mullany B, Cooke AL (2007) On the measurement of temperature in material removal processes. CIRP Ann Manuf Technol 56(2):581–604CrossRefGoogle Scholar
  16. Gandarias E, Dimov S, Pham DT, Ivanov A, Popov K, Lizarralde R, Arrazola PJ (2006) New methods for tool failure detection in micromilling. Proc IMechE Part B: J Eng Manuf 220(B2):137–144CrossRefGoogle Scholar
  17. Griffin J, Chen X (2009) Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. Int J Adv Manuf Technol 45(11–12):1152–1168CrossRefGoogle Scholar
  18. Ibrahim Nur T, Mekdeci C, McLaughlin C (1995) Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN). Int J Mach Tool Manuf 35(8):1137–1147CrossRefGoogle Scholar
  19. Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15(10):711–721CrossRefGoogle Scholar
  20. Jemielniaka K, Tetib R, Kossakowskaa J, Segretob T (2006) Innovative signal processing for cutting force based chip form prediction. In: 2nd Virtual Integration Conference on IPROMS, Ischia, pp 7–12Google Scholar
  21. Kim H-Y, Ahn J-H (2002) Chip disposal state monitoring in drilling using neural network based spindle motor power sensing. Int J Mach Tools Manuf 42(10):1113–1119CrossRefMathSciNetGoogle Scholar
  22. Kim J-D, Kim D-S (1997) Development of a combined-type tool dynamometer with a piezo-film accelerometer for an ultra-precision lathe. J Mater Process Technol 71(3):360–366CrossRefGoogle Scholar
  23. Kim HY, Ahn JH, Kim SH, Takata S (2002) Real-time drill wear estimation based on spindle motor power. J Mater Process Technol 124(3):267–273CrossRefGoogle Scholar
  24. Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312(4–5):672–693CrossRefGoogle Scholar
  25. Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mech Syst Signal Process 23(5):1704–1718CrossRefGoogle Scholar
  26. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Indus 34(1):55–72CrossRefGoogle Scholar
  27. Lanzetta M (2001) A new flexible high-resolution vision sensor for tool condition monitoring. J Mater Process Technol 119(1–3):73–82CrossRefGoogle Scholar
  28. Latha B, Senthilkumar VS (2010) Modeling and analysis of surface roughness parameters in drilling GFRP composites using fuzzy logic. Mater Manuf Process 25(8):817–827CrossRefGoogle Scholar
  29. Le Coz G, Marinescu M, Devillez A, Dudzinski D, Velnom L (2012) Measuring temperature of rotating cutting tools: application to MQL drilling and dry milling of aerospace alloys. Appl Thermal Eng 36:434–441CrossRefGoogle Scholar
  30. Li S, Elbestawi MA (1996) Fuzzy clustering for automated tool condition monitoring in machining. Mech Syst Signal Process 10(5):533–550CrossRefGoogle Scholar
  31. Li HZ, Zeng H, Chen XQ (2006) An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts. J Mater Process Technol 180(1–3):296–304Google Scholar
  32. Li HZ, Chen XQ, Zeng H, Li XP (2007) Embedded tool condition monitoring for intelligent machining. Int J Comp Appl Technol 28(1):74–81CrossRefMathSciNetGoogle Scholar
  33. Li HZ, Albrecht A, Chen XQ (2009) A tool wear observer model for flank wear monitoring in the milling of nickel-based alloys. Int J Mech Manuf Syst 2(5/6):620–637Google Scholar
  34. Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-of-the-Art. J Manuf Sci Eng 126(2):297–310CrossRefGoogle Scholar
  35. Marinescu I, Axinte D (2009) A time-frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously. Int J Mach Tool Manuf 49(1):53–65CrossRefGoogle Scholar
  36. Nowicki B, Jarkiewicz A (1998) In-process surface roughness measurement using fringe field capacitive (FFC) method. Int J Mach Tool Manuf 38(5–6):725–732CrossRefGoogle Scholar
  37. Ozel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tool Manuf 45(4–5):467–479CrossRefGoogle Scholar
  38. Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tool Manuf 47(14):2140–2152CrossRefGoogle Scholar
  39. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43(1–2):40–51CrossRefGoogle Scholar
  40. Susanto V, Chen JC (2003) Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int J Adv Manuf Technol 21(3):186–192Google Scholar
  41. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739CrossRefGoogle Scholar
  42. Tsai Y-H, Chen JC, Lou S-J (1999) An in-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tool Manuf 39(4):583–605CrossRefGoogle Scholar
  43. Ueda T, Hosokawa A, Oda K, Yamada K (2001) Temperature on flank face of cutting tool in high speed milling. CIRP Ann Manuf Technol 50(1):37–40CrossRefGoogle Scholar
  44. Wang L, Mehrabi MG, Kannatey-Asibu E Jr (2002) Hidden Markov model-based tool wear monitoring in turning. Trans ASME J Manuf Sci Eng 124(3):651–658CrossRefGoogle Scholar
  45. Wang X, Wang W, Huang Y, Nguyen N, Krishnakumar K (2008) Design of neural network-based estimator for tool wear modeling in hard turning. J Intell Manuf 19(4):383–396CrossRefGoogle Scholar
  46. Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713–719CrossRefGoogle Scholar
  47. Zhang J, Zhang C, Guo S, Zhou L (2012) Research on tool wear detection based on machine vision in end milling process. Product Eng 6(4–5):431–437CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.School of Mechanical and Manufacturing EngineeringUNSWSydneyAustralia

Personalised recommendations