Abstract
The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.
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Chryssolouris G (2006) Manufacturing systems—theory and practice, 2nd edn. Springer, New York
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
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
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
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
Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tools Manufac 42:997–1010
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
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
Lim G (1995) Tool-wear monitoring in machine turning. J Mat Process Technol 51:25–36
Smith GT (1989) Advanced machining, IFS Publications. Springer, UK
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
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
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
Jantunen E. (2002) A solution for tool wear diagnosis, in: proceedings, International Journal of Machine Tools & Manufacture 42, 997–1010, 1999, pp. 95–104
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
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
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
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
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
Principe C, Yoon T (1991) A new algorithm for the detection of tool breakage in milling. Int J Mach Tools Manufac 31:443–454
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
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
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
Kluft, W. (1983) Werkzeuguberwachungssysteme furs die Drehbearbeitung, doctoral thesis, RWTH Aachen
Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tools Manuf 39:105–122
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
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
Chryssolouris G (1982) Effects of machine-tool-workpiece stiffness on the wear behavior of superhard cutting materials. CIRP Ann 31(1):65–69
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
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
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
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
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
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
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
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
Santochi M, Dini G, Tantussi G (1997) A sensor-integrated tool for cutting force monitoring. CIRP Ann 46/1:49
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
Von Nedeß C., T. Himburg (1986) Automatisierte Uberwachung des Bohrens, VDI-Z, Bd 128 (17), 651–657
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
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
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
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
Čuš F, Župerl U (2011) Real-time cutting tool condition monitoring in milling. Strojniški vestnik - J Mech Eng 57(2):142–150
Zheng G., Zhao J., Li Z., Cheng X., Li L.(2014) Fractal characterization of the friction forces of a graded ceramic tool material
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
FoFdation Deliverable 4.2 (May 2013) An intelligent adaptive & sustainable approach, ARTIS
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
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
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
http://www.google.com/patents/US6638609, retrieved July 15, 2014
Kalpajian S (1996) Manufacturing processes for engineering materials, 3rd edn. Addison-Wesley, Longman
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
P Fromme1 and C Rouge (2011) Directivity of guided ultrasonic wave scattering at notches and cracks, Journal of Physics: Conference Series 269 012018
MatWeb’s searchable database of material properties, http://www.matweb.com/index.aspx
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
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Stavropoulos, P., Papacharalampopoulos, A., Vasiliadis, E. et al. Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol 82, 509–521 (2016). https://doi.org/10.1007/s00170-015-7317-6
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DOI: https://doi.org/10.1007/s00170-015-7317-6