Tool life predictions in milling using spindle power with the neural network technique

Journal of Manufacturing Processes

Volumn 22, Issue , 2016, Pages 161-168

  Drouillet, Cyril ^a,b   Karandikar, Jaydeep ^a,c   Nath, Chandra ^a,d   Journeaux, Anne Claire ^a,b   El Mansori, Mohamed ^b   Kurfess, Thomas ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b ARTS ET MÉTIERS PARISTECH   (France)

^c GE GLOBAL RESEARCH   (United States)

^d HITACHI AMERICA LTD   (United States)

Author keywords

End milling; Neural network; Spindle power signal; Tool condition monitoring; Tool life; Uncertainty

Indexed keywords

CONDITION MONITORING; CURVE FITTING; FORECASTING; MILLING (MACHINING); NEURAL NETWORKS; STAINLESS STEEL; TIME DOMAIN ANALYSIS; WEAR OF MATERIALS;

END MILLING; SPINDLE POWER; TOOL CONDITION MONITORING; TOOL LIFE; UNCERTAINTY;

CUTTING TOOLS;

EID: 84977483958     PISSN: 15266125     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmapro.2016.03.010     Document Type: Article

References (34)

1. [1] Kegg, R.L., One-line machine and process diagnostics. CIRP Ann 33 (1984), 469–473.
2. [2] Kurada, S., Bradley, C., A review of machine vision sensors for tool condition monitoring. Comput Ind 34:1 (1997), 55–72.
3. [3] Marksberry, P.W., Jawahir, I.S., A comprehensive tool-wear/tool-life performance model in the evaluation of NDM (near dry machining) for sustainable manufacturing. Int J Mach Tools Manuf 48:7–8 (2008), 878–886, 10.1016/j.ijmachtools.2007.11.006.
4. [4] Abellan-Nebot, J.V., Romero Subirón, F., A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47:1–4 (2009), 237–257, 10.1007/s00170-009-2191-8.
5. [5] Cho, D.-W., Lee, S.J., Chu, C.N., The state of machining process monitoring research in Korea. Int J Mach Tools Manuf 39:11 (1999), 1697–1715, 10.1016/S0890-6955(99)00026-7.
6. [6] Kurada, S., Bradley, C., A review of machine vision sensors for tool condition monitoring. Comput Ind 34:1 (1997), 55–72, 10.1016/S0166-3615(96)00075-9.
7. [7] Rehorn, A.G., Jiang, J., Orban, P.E., State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26:7–8 (2004), 693–710, 10.1007/s00170-004-2038-2.
8. [8] Giardini, C., Ceretti, E., Maccarini, G., A neural network architecture for tool wear detection through digital camera observations. Kuljanic, E., (eds.) Advanced manufacturing systems and technology SE – 14, vol. 372, 1996, Springer, Vienna, 137–144, 10.1007/978-3-7091-2678-3_14.
9. [9] Shahabi, H.H., Ratnam, M.M., In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision. Int J Adv Manuf Technol 40:11–12 (2008), 1148–1157, 10.1007/s00170-008-1430-8.
10. [10] Xiong, G., Liu, J., Avila, A., Cutting tool wear measurement by using active contour model based image processing. 2011 IEEE international conference on mechatronics and automation, 2, 2011, 670–675, 10.1109/ICMA.2011.5985741.
11. [11] Cuppini, D., Errico, G.D., Tool image processing with applications to unmanned metal-cutting. A computer vision system for wear sensing and failure detection. 1986, 416.
12. [12] Yeo, S.H., Khoo, L.P., Neo, S.S., Tool condition monitoring using reflectance of chip surface and neural network. J Intell Manuf, 2000, 507–514.
13. [13] Obikawa, T., Shinozuka, J., Monitoring of flank wear of coated tools in high speed machining with a neural network ART2. Int J Mach Tools Manuf 44:12–13 (2004), 1311–1318, 10.1016/j.ijmachtools.2004.04.021.
14. [14] Dong, J., Subrahmanyam, K.V.R., Wong, Y.S., Hong, G.S., Mohanty, A.R., Bayesian-inference-based neural networks for tool wear estimation. Int J Adv Manuf Technol 30:9–10 (2005), 797–807, 10.1007/s00170-005-0124-8.
15. [15] Huang, B.P., Chen, J.C., Neural network-based tool breakage monitoring system for end milling operations. J Ind Technol 16:2 (2000), 1–7.
16. [16] Lin, S.C., Lin, R.J., Tool wear monitoring in face milling using force signals. Wear 198 (1996), 136–142.
17. [17] Jemielniak K, Kosmol J, n.d. Tool and process monitoring – state of art and future prospects.
18. [18] Yesilyurt, I., Ozturk, H., Tool condition monitoring in milling using vibration analysis. Int J Prod Res 45:4 (2007), 1013–1028, 10.1080/00207540600677781.
19. [19] Fu, P., Li, W.L., Zhu, L.Q., Cutting tool wear monitoring based on wavelet denoising and fractal theory. Appl Mech Mater 48–49 (2011), 349–352, 10.4028/www.scientific.net/AMM.48-49.349.
20. [20] Zhang, J.Z., Chen, J.C., Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Technol 39:1–2 (2007), 118–128, 10.1007/s00170-007-1186-6.
21. [21] Bahr, B., Motavalli, S., Arfi, T., Sensor fusion for monitoring machine tool conditions. Int J Comput Integr Manuf 10:5 (1997), 314–323, 10.1080/095119297131066.
22. [22] Li, X.L., Yuan, Z.J., Tool wear monitoring with wavelet packet transform fuzzy clustering method. Wear 219 (1998), 145–154.
23. [23] Li, X.L., Dong, S., Yuan, Z.J., Discrete wavelet transform for tool breakage monitoring. Int J Mach Tools Manuf 39 (1999), 1935–1944.
24. [24] Lee, B., Tarng, Y.S., Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. Int J Adv Manuf Technol 15:4 (1999), 238–243.
25. [25] Du, R., Liu, Y., Xu, Y., Tool condition monitoring using transition fuzzy probability. 3rd international conference on metal cutting and high speed machining, 2001, 375–393.
26. [26] Shao, H., Wang, H.L., Zhao, X.M., A cutting power model for tool wear monitoring in milling. Int J Mach Tools Manuf 44:14 (2004), 1503–1509, 10.1016/j.ijmachtools.2004.05.003.
27. [27] Ghosh, N., Ravi, Y.B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A.R., et al. Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21 (2007), 466–479.
28. [28] Rooij, A.J.F., Van Johnson, R.P., Jain, L.C., Neural network training using genetic algorithms. 1996, World Scientific Publishing Co., Inc., River Edge, NJ, USA.
29. [29] Theodosis, P., Neural networks: interpolation and curve fitting. 2007, 1–12.
30. [30] Hill, T., Marquez, L., O'Connor, M., Remus, W., Artificial neural network models for forecasting and decision making. Int J Forecast 10:1 (1994), 5–15.
31. [31] Stergiou C. Why use a neural network? http://www.doc.ic.ac.uk/∼nd/surprise_96/journal/vol1/cs11/article1.html [accessed 15.12.15].
32. [32] Tu, J.V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49:11 (1996), 1225–1231.
33. [33] Stathakis, D., How many hidden layers and nodes?. Int J Remote Sens 30:8 (2009), 2133–2147.
34. [34] Karandikar, J., McLeay, T., Turner, S., Schmitz, T., Remaining useful tool life predictions using Bayesian inference. Volume 2: Systems; micro and nano technologies; sustainable manufacturing. 2013, June 10, ASME, 10.1115/MSEC2013-1152.