Tool wear predictability estimation in milling based on multi-sensorial data

International Journal of Advanced Manufacturing Technology

Volumn 82, Issue 1-4, 2016, Pages 509-521

  Stavropoulos, P ^a,b   Papacharalampopoulos, A ^a   Vasiliadis, E ^a   Chryssolouris, G ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b HELLENIC AIR FORCE ACADEMY   (Greece)

Author keywords

Fused sensorial signals; Monitoring; Pattern recognition; Predictability; Tool wear

Indexed keywords

DIGITAL STORAGE; MILLING (MACHINING); MONITORING; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; REGRESSION ANALYSIS; SYSTEMS ENGINEERING; WEAR OF MATERIALS;

ECONOMIC PRODUCTIVITY; INDUSTRIAL MANUFACTURING PROCESS; MACHINING PROCESS; PREDICTABILITY; RELIABLE OPERATION; SIMULTANEOUS DETECTION; TOOL WEAR; VIBRATION ACCELERATION;

CUTTING TOOLS;

EID: 84953839585     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-015-7317-6     Document Type: Article

References (56)

1. Chryssolouris G (2006) Manufacturing systems—theory and practice, 2nd edn. Springer, New York
2. Ji C, Liu Z, Ai X (2014) Effect of cutter geometric configuration on aerodynamic noise generation in face milling cutters. Appl Acoust 75:43–51
3. Larreina J., Gontarz A., Giannoulis C., Nguyen V.K., Stavropoulos P., Sinceri B. (2013) Smart Manufacturing Execution System (SMES): The possibilities of evaluating the sustainability of a production process, (GCSM) 11th Global Conference on Sustainable Manufacturing, 23-25 September, Berlin, Germany, pp.517–522
4. Wright P.K., Trent E.M. (1974) Metallurgical appraisal of wear mechanisms and processes on high-speed-steel cutting tools, Metals Technology, January 13–23
5. Tarasov SY, Rubtsov VE, Kolubaeva EA (2014) A proposed diffusion-controlled wear mechanism of alloy steel friction stir welding (FSW) tools used on an aluminum alloy. Wear 318(1–2):130–134
6. Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tools Manufac 42:997–1010
7. Salonitis K, Kolios A (2014) Reliability assessment of cutting tool life based on surrogate approximation methods. Int J Adv Manuf Technol 71(5–8):1197–1208
8. Li H, He G, Qin X, Wang G, Lu C, Gui L (2014) Tool wear and hole quality investigation in dry helical milling of Ti-6Al-4V alloy. Int J Adv Manuf Technol 71:1511–152
9. Lim G (1995) Tool-wear monitoring in machine turning. J Mat Process Technol 51:25–36
10. Smith GT (1989) Advanced machining, IFS Publications. Springer, UK
11. Wang W. H., Hong G. S., Wong Y. S. and Zhu K. P. (2007) Sensor fusion for online tool condition monitoring in milling, Vol. 45, No. 21, 1 November, 5095–5116
12. Stavropoulos P, Stournaras A, Chryssolouris G (2009) On the design of a monitoring system for desktop micro-milling machines. Int J Nanomanufac 3(1/2):29–39
13. Kavaratzis Y, Maiden JD (1989) System for real time process monitoring and adaptive control during CNC deep hole drilling, in: proceedings of Comadem ‘89. Kogan Page, London, pp 148–152
14. Jantunen E. (2002) A solution for tool wear diagnosis, in: proceedings, International Journal of Machine Tools & Manufacture 42, 997–1010, 1999, pp. 95–104
15. Li X (1999) On-line detection of the breakage of small diameter drills using current signature wavelet transform. Int J Mach Tools Manufac 39(1):157–164
16. Lee DE, Hwang I, Valente CMO, Oliveira JFG, Dornfeld DA (2006) Precision manufacturing process monitoring with acoustic emission. Int J Mach Tools Manufac 46:176–188
17. Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102
18. Cao H, Chena X, Zia Y (2008) End milling tool breakage detection using lifting scheme and Mahalanobis distance. Int J Mach Tools Manufac 48:141–151
19. Chryssolouris G., Guillot M., Domroese M. (1987) Tool wear estimation for intelligent machining, symposium on intelligent control, ASME Winter Annual Meeting, Boston, Massachusetts, pp. 35–43
20. Principe C, Yoon T (1991) A new algorithm for the detection of tool breakage in milling. Int J Mach Tools Manufac 31:443–454
21. Stavropoulos P., Salonitis K., Stournaras A., Pandremenos J., Paralikas J., Chryssolouris G. (2007) Experimental investigation of micro-milling process quality, 40th CIRP International Seminar on Manufacturing Systems, Liverpool
22. Ertekin YM, Kwon Y, Tseng B (2003) Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. Int J Mach Tools Manufac 43:897–904
23. Stavropoulos P., Salonitis K., Stournaras A., Pandremenos J., Paralikas J., Chryssolouris G. (2007), Advances and challenges for tool condition monitoring in micro-milling, IFAC Workshop on Manufacturing Modelling, Management and Control, pp. 157–162
24. Kluft, W. (1983) Werkzeuguberwachungssysteme furs die Drehbearbeitung, doctoral thesis, RWTH Aachen
25. Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tools Manuf 39:105–122
26. Ritou M, Garniera S, Fureta B, Hascoet JY (2014) Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mech Syst Signal Process 44(1–2):211–220
27. Chryssolouris G., Domroese M. (1988) Sensor integration for tool wear estimation in machining, symposium on sensors and controls for manufacturing, ASME Winter Annual Meeting, Chicago, Illinois, USA, pp. 115–123
28. Chryssolouris G (1982) Effects of machine-tool-workpiece stiffness on the wear behavior of superhard cutting materials. CIRP Ann 31(1):65–69
29. 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, 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering
30. Ee KC, Li PX, Balaji AK, Jawahir IS, Stevenson R (2006) Performance-based predictive models and optimization methods for turning operations and applications: part 1—tool wear/tool life in turning with coated grooved tools. J Manuf Process 8(1):54–66
31. Doukas C., Stavropoulos P., Papacharalampopoulos A., Foteinopoulos P., Vasiliadis E., Chryssolouris G. (2013) “On the estimation of tool-wear for milling operations based on multisensorial data”, (CIRP CMMO) Procedia CIRP, 14th CIRP Conference on Modelling of Machining Operations, 13-14 June, Turin, Italy
32. Segreto, T., Teti R. (2008) “Sensor fusion of acoustic emission and cutting force for tool wear monitoring during composite materials machining”, 6th CIRP International Conference on Intelligent Computation in Manufacturing Engineering—CIRP ICME’08, 23–25 July 2008, Naples, Italy, p. 221
33. Segreto T, Simeone A, Teti R (2012) “Sensor fusion for tool state classification in nickel superalloy high performance cutting”, 5th CIRP international conference on high performance cutting. Procedia CIRP 1:593
34. Balakrishnan, P., Trabelsy, H., Kannatey-Asibu, E., Emel, E. A. (1989) “Sensor fusion approach to cutting tool monitoring”, Proc. 15th NSF Conf. on Production Research and Technology, SME, University of California, Berkeley, p. 101
35. Chiu, S.L., Morley, D.J., Martin, J. (1987) “Sensor data fusion on a parallel processor”, Proc. IEEE Int. Conf. on Robotics and Automation, Raleigh, NC, p. 1629
36. Rangwala S, Dornfeld DA (1987) “Integration of sensors via neural networks for detection of tool wear states”. Proc Winter Ann Meet ASME, PED 25:109
37. Santochi M, Dini G, Tantussi G (1997) A sensor-integrated tool for cutting force monitoring. CIRP Ann 46/1:49
38. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466–479
39. Von Nedeß C., T. Himburg (1986) Automatisierte Uberwachung des Bohrens, VDI-Z, Bd 128 (17), 651–657
40. Tansel IN, Mekdeci C, Rodriguez O, Uragun B (1993) Monitoring drill conditions with wavelet based encoding and neural network. Int J Mach Tools Manufac 33(4):559–575
41. Ertunc H. M., Loparo K.A., Ocak H. (2001) Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs), Int J Mach Tools Manufac 411363–1384
42. Rao KV, Murthy BSN, Rao NM (2014) Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network. Measurement 51:63–70
43. Roth J. T., Pandit S. M. (1999) Monitoring end-mill wear and predicting tool failure using accelerometers. J Manufac Sci Eng Volume 121, Issue 4
44. Čuš F, Župerl U (2011) Real-time cutting tool condition monitoring in milling. Strojniški vestnik - J Mech Eng 57(2):142–150
45. Zheng G., Zhao J., Li Z., Cheng X., Li L.(2014) Fractal characterization of the friction forces of a graded ceramic tool material
46. Wang GF, Yang YW, Zhang YC, Xie QL (2014) Vibration sensor based tool condition monitoring using support vector machine and locality preserving projection. Sensors Actuators A 209:24–32
47. FoFdation Deliverable 4.2 (May 2013) An intelligent adaptive & sustainable approach, ARTIS
48. Tansel IN, Arkan TT, Bao WY, Mahendrakar N, Shisler B, Smith D, McCool M (2000) Tool wear estimation in micro-machining. part I: tool usage–cutting force relationship. Int J Mach Tools Manufac 40:599–608
49. Chakraborty P, Asfour S, Cho S, Onar A, Lynn M (2008) Modeling tool wear progression by using mixed effects modeling technique when end-milling AISI 4340 steel. J Mater Process Technol 205:190–202
50. Da Silva MB, Naves VTG, De Melo JDB, De Andrade CLF, Guesser WL (2011) Analysis of wear of cemented carbide cutting tools during milling operation of gray iron and compacted graphite iron. Wear 271:2426–2432
51. http://www.google.com/patents/US6638609, retrieved July 15, 2014
52. Kalpajian S (1996) Manufacturing processes for engineering materials, 3rd edn. Addison-Wesley, Longman
53. Papacharalampopoulos A, Stavropoulos P, Doukas C, Foteinopoulos P, Chryssolouris G (2013) Acoustic emission signal through turning tools: a computational study. Procedia CIRP 8(2013):426–431
54. P Fromme1 and C Rouge (2011) Directivity of guided ultrasonic wave scattering at notches and cracks, Journal of Physics: Conference Series 269 012018
55. MatWeb’s searchable database of material properties, http://www.matweb.com/index.aspx
56. Li B, Cai H, Mao X, Huang J, Luo B (2013) Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis. Int J Mach Tools Manufac 71:26–40