Abstract
Increasing demand for precision-manufactured parts for high-tech applications in aerospace, nuclear power, transportation, etc. continually drives the advancement of precision manufacturing technologies. As the precision and quality of manufactured parts are significantly affected by the performance of the machine tools, accurate and reliable condition monitoring, performance prediction, and maintenance of machine tools become one important part of precision manufacturing. In this chapter, a stochastic modeling technique is presented for prediction of machine tool performance degradation based on sensing data from the tool wear. Specifically, to account for the nonlinear and non-Gaussian characteristic of operating and environmental conditions on the wear propagation and machine performance degradation, particle filter (PF) that approximates probability distributions through a set of weighted particles is investigated. To improve the reliability of time-varying degradation tracking and prediction, an adaptive resampling particle filter method is developed. Specifically, particles are recursively updated and resampled from the neighborhoods that are determined by particles’ estimation performance from the last iteration, to characterize the temporal variation in the tool degradation rates. This leads to improved tracking and prediction accuracy with progressively narrowed confidence interval. The developed method has been experimentally evaluated using a set of benchmark data that were measured on a high-speed CNC machine.
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References
Agogino A, Goebel K (2007) Milling data set. NASA Ames Prognostics Data Repository, NASA Ames Research Center
An D, Choi JH, Kim NH (2013) Options for prognostics methods: a review of data-driven and physics-based prognostics. In: 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Boston, p 1940
Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188
Aslantaş K, Taşgetiren S (2004) A study of spur gear pitting formation and life prediction. Wear 257(11):1167–1175
Astakhov VP (2007) Effects of the cutting feed, depth of cut, and workpiece (bore) diameter on the tool wear rate. Int J Adv Manuf Technol 34(7–8):631–640
Baraldi P, Mangili F, Zio E (2013) Investigation of uncertainty treatment capability of model- based and data-driven prognostic methods using simulated data. Reliab Eng Syst Saf 112:94–108
Byrne G, O’Donnell GE (2007) An integrated force sensor solution for process monitoring of drilling operations. CIRP Ann Manuf Technol 56(1):89–92
Caesarendra W, Widodo A, Thom PH, Yang B, Setiawan JD (2011) Combined probability approach and indirect data-driven method for bearing degradation prognostics. IEEE Trans Reliab 60(1):14–20
Dornfeld DA, Lee Y, Chang A (2003) Monitoring of ultraprecision machining processes. Int J Adv Manuf Technol 21(8):571–578
Doucet A, Godsill S, Andrieu C (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput 10(3):197–208
Doucet A, Gordon NJ, Krishnamurthy V (2001) Particle filters for state estimation of jump Markov linear systems. IEEE Trans Signal Process 49(3):613–624
Eguchi S, Copas J (2006) Interpreting Kullback–Leibler divergence with the Neyman–Pearson lemma. J Multivar Anal 97(9):2034–2040
Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M, Helu M (2015) Cloud-enabled prognosis for manufacturing. CIRP Ann Manuf Technol 64(2):749–772
Gašperin M, Juričić Ɖ, Boškoski P, Vižintin J (2011) Model-based prognostics of gear health using stochastic dynamical models. Mech Syst Signal Process 25(2):537548
Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F Radar Signal Process 140(2):107113
Heng A, Zhang S, Tan AC, Mathew J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process 23(3):724–739
Hoffman EG (1984) Fundamentals of tool design. Society of Manufacturing Engineers, Dearborn
Huang Y, Liang S (2004) Modeling of CBN tool flank wear progression in finish hard turning. Trans ASME J Manuf Sci Eng 126:98–106
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510
Karandikar JM, Abbas AE, Schmitz TL (2013) Tool life prediction using random walk Bayesian updating. Mach Sci Technol 17(3):410–442
Karpuschewski B, Schmidt K, Beno J, Mankova I, Frohumller R, Prilukova J (2015) An approach to the microscopic study of wear mechanism during hard turning with coated ceramics. Wear 342–343:222–233
Le SK, Fouladirad M, Barros A, Levrat E, Lung B (2013) Remaining useful life estimation based on stochastic deterioration models: a comparative study. Reliab Eng Syst Saf 112:165–175
Li B (2012) A review of tool wear estimation using theoretical analysis and numerical simulation technologies. Int J Refract Met Hard Mater 35:143–151
Li X, Lim BS, Zhou JH, Huang S, Phua SJ, Shaw KC, Er MJ (2009) Fuzzy neural network modelling for tool wear estimation in dry milling operation. In: Annual conference of the prognostics and health management society, San Diego, pp 1–11
Luo X, Cheng K, Holt R, Liu X (2005) Modeling flank wear of carbide tool insert in metal cutting. Wear 259:1235–1240
Marksberry PW, Jawahir IS (2008) 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):878–886
Mehnen J, Tinsley L, Roy R (2014) Automated in-service damage identification. CIRP Ann Manuf Technol 63(1):33–36
Mills B, Rdeford A (1983) Machinability of engineering materials. Applied Science, London
Paris PC (1961) A rational analytic theory of fatigue. Trends Eng 13:9–14
Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50(1–4):297–313
Poulachon G, Moisan A, Jawhir IS (2001) Tool-wear mechanisms in hard turning with polycrystalline cubic boron nitride tools. Wear 250(1–12):576–586
Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7–8):693–710
Si X, Wang W, Hu C, Zhou D (2011) Remaining useful life estimation-a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14
Sikorska JZ, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836
Sun B, Zeng S, Kang R, Pecht MG (2012) Benefits and challenges of system prognostics. IEEE Trans Reliab 61(2):323–335
Teti R, Jemielniak K, Donnell GO, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739
Wang P, Gao RX (2015) Adaptive resampling-based particle filtering for tool life prediction. J Manuf Syst 37:528–534
Wang P, Gao RX (2016a) Stochastic tool wear prediction for sustainable manufacturing. Procedia CIRP 48:236–241
Wang P, Gao RX (2016b) Markov nonlinear system estimation for engine performance tracking. ASME J Eng Gas Turbines Power 138(9):091201
Wang P, Gao RX (2017) Automated performance tracking for heat exchangers in HVAC. IEEE Trans Autom Sci Eng 14(2):634–645
Wang J, Wang P, Gao RX (2015) Enhanced particle filter for tool wear prediction. J Manuf Syst 36:35–45
Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96:5–8
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Gao, R.X., Wang, P., Yan, R. (2019). Machine Tool Prognosis for Precision Manufacturing. In: Gao, W. (eds) Metrology. Precision Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-10-4912-5_8-1
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DOI: https://doi.org/10.1007/978-981-10-4912-5_8-1
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