Abstract
High-performance component manufacturing has increasing needs of robotic grinding process that can achieve accurate material removal. This article proposes a novel material removal model for robotic belt grinding of Inconel 718 based on acoustic sensing and machine learning. The sound signal is collected online by an audio sensor during the grinding process. A novel method to identify the idle running period and eliminate noise is developed using discrete wavelet decomposition (DWD) and fast Fourier transformation (FFT). Statistical features are extracted from each clean acoustic signal segment to better represent and quantify grinding process. A new k-fold eXtreme Gradient Boosting (k-fold-XGBoost) algorithm after training and optimization is integrated into the material removal (MR) model. The test results indicate that the values forecasted by the model are consistent with the measured values. The mean absolute percentage error (MAPE) of material removal evaluated by the model is 4.373%, which shows a better performance than the reported results which are in the range of 6.4 to 8.72%. In comparison with other prediction models, such as optimally pruned extreme learning machine and random forest and support vector regression, k-fold-XGBoost model shows superior results for the same datasets. It can be concluded that the proposed method based on acoustic signal and the ensemble learning model is effective in predicting the material removal despite the complicated grinding environment.
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References
Brinksmeier E, Heinzel C, Wittmann M (1999) Friction, cooling and lubrication in grinding. CIRP Ann Manuf Technol 48:581–598. https://doi.org/10.1016/S0007-8506(07)63236-3
Kopac J, Krajnik P (2006) High-performance grinding-a review. J Mater Process Technol 175:278–284. https://doi.org/10.1016/j.jmatprotec.2005.04.010
Wegener K, Hoffmeister HW, Karpuschewski B, Kuster F, Hahmann WC, Rabiey M (2011) Conditioning and monitoring of grinding wheels. CIRP Ann - Manuf Technol 60:757–777. https://doi.org/10.1016/j.cirp.2011.05.003
Xiaoqi C, Zhiming G, Han H (2000) Development of robotic system for 3D profile grinding and polishing (Vol 12). SIMTech Technical Report
Huang H, Gong ZM, Chen XQ, Zhou L (2003) SMART robotic system for 3D profile turbine vane airfoil repair. Int J Adv Manuf Technol 21:275–283. https://doi.org/10.1007/s001700300032
Akca E, Gürsel A (2015) A review on superalloys and IN718 nickel-based INCONEL superalloy. Periodicals Eng Nat Sci (PEN) 3(1)
Dudzinski D, Devillez A, Moufki A, Larrouquère D, Zerrouki V, Vigneau J (2004) A review of developments towards dry and high speed machining of Inconel 718 alloy. Int J Mach Tools Manuf 44:439–456. https://doi.org/10.1016/S0890-6955(03)00159-7
Thakur A, Gangopadhyay S (2016) State-of-the-art in surface integrity in machining of nickel-based super alloys. Int J Mach Tools Manuf 100:25–54. https://doi.org/10.1016/j.ijmachtools.2015.10.001
Hammann G (2013) Modellierung des Abtragsverhaltens elastischer, robotergeführter Schleifwerkzeuge, vol 123. Springer-Verlag
Zhang X, Kuhlenkötter B, Kneupner K (2005) An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding. Int J Mach Tools Manuf 45:641–648. https://doi.org/10.1016/j.ijmachtools.2004.10.006
Jin XL, Zhang LC (2012) A statistical model for material removal prediction in polishing. Wear 274–275:203–211. https://doi.org/10.1016/j.wear.2011.08.028
Preston FW (1927) The theory and design of plate glass polishing machines. J Glas Technol 11:214–256
Jo W, Lee S, Kim H, Lee T, Lee S (2016) A study of material removal characteristics by friction monitoring system of sapphire wafer in single side DMP. J Korean Soc Tribol Lubr Eng 32:56–60
Zhang X, Cabaravdic M, Kneupner K, Kuhlenkoetter B (2004) Real-time simulation of robot controlled belt grinding processes of sculptured surfaces. Int J Adv Robot Syst 1:109–114. https://doi.org/10.5772/5627
Cabaravdic M, Kuhlenkötter B (2005) Bandschleifprozesse optimieren. Metalloberfläche 59:44–47
Ren X, Cabaravdic M, Zhang X, Kuhlenkötter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf 47:962–970. https://doi.org/10.1016/j.ijmachtools.2006.07.002
Ren X, Kuhlenkötter B (2008) Real-time simulation and visualization of robotic belt grinding processes. Int J Adv Manuf Technol 35:1090–1099. https://doi.org/10.1007/s00170-006-0791-0
Takata S, Ahn JH, Miki M, Miyao Y, Sata T (1986) A sound monitoring system for fault detection of machine and machining states. CIRP Ann Manuf Technol 35:289–292. https://doi.org/10.1016/S0007-8506(07)61890-3
Lu MC, Wan BS (2013) Study of high-frequency sound signals for tool wear monitoring in micromilling. Int J Adv Manuf Technol 66:1785–1792. https://doi.org/10.1007/s00170-012-4458-8
Cheng K (ed) (2008) Machining dynamics: fundamentals, applications and practices. Springer Science & Business Media
Lin Y-K, Wu B-F, Chen C-M (2018) Characterization of grinding wheel condition by acoustic emission signals. 2018 Int Conf Syst Sci Eng 1–6. https://doi.org/10.1109/ICSSE.2018.8520249
Souza RVCG, Rocha CA, Marchi M et al (2014) Tool condition monitoring dressing using vibration signals and neural networks models. Proc XX Braz Congr Autom 20:1026–1033
Susič E, Grabec I (2000) Characterization of the grinding process by acoustic emission. Int J Mach Tools Manuf 40:225–238. https://doi.org/10.1016/S0890-6955(99)00055-3
Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23–45. https://doi.org/10.1080/21693277.2016.1192517
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. https://doi.org/10.1016/j.ymssp.2005.10.010
Ak R, Helu MM, Rachuri S (2015) Ensemble neural network model for predicting the energy consumption of a milling machine. In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. V004T05A056-V004T05A056). American Society of Mechanical Engineers
Carbonneau R, Laframboise K, Vahidov R (2008) Application of machine learning techniques for supply chain demand forecasting. Eur J Oper Res 184:1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004
Zhong G, Kang M, Yang S (2017) Precision obtained using an artificial neural network for predicting the material removal rate in ultrasonic machining. Appl Sci 7. https://doi.org/10.3390/app7121268
Shen J, Pei ZJ, Lee ES (2008) Support vector fuzzy adaptive network in the modelling of material removal rate in rotary ultrasonic machining. J Manuf Sci Eng 130:041005. https://doi.org/10.1115/1.2951935
Chen J, Chen H, Xu J, Wang J, Zhang X, Chen X (2018) Acoustic signal-based tool condition monitoring in belt grinding of nickel-based superalloys using RF classifier and MLR algorithm. Int J Adv Manuf Technol 98(1–4):859–872
Zhang X, Chen H, Xu J, Song X, Wang J, Chen X (2018) A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine. J Mater Process Technol 260:9–19. https://doi.org/10.1016/j.jmatprotec.2018.05.013
Zhang X, Kneupner K, Kuhlenk B (2006) A new force distribution calculation model for high-quality production processes. 726–732. https://doi.org/10.1007/s00170-004-2229-x
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739. https://doi.org/10.1016/j.cirp.2010.05.010
Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210:713–719. https://doi.org/10.1016/j.jmatprotec.2009.11.007
Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47:2140–2152. https://doi.org/10.1111/j.1439-0388.2011.00967.x
Terchi A, Au YHJ (2001) Acoustic emission signal processing. Meas Control 34:240–244. https://doi.org/10.1177/002029400103400804
Zeng H, Chen X (2002) Acoustic emission sensing and signal processing for machining monitoring and control. In: Advanced Automation Techniques in Adaptive Material Processing. World Sci, pp 91–124
Grosse CU, Finck F, Kurz JH, Reinhardt HW (2004) Improvements of AE technique using wavelet algorithms, coherence functions and automatic data analysis. Constr Build Mater 18:203–213. https://doi.org/10.1016/j.conbuildmat.2003.10.010
Kwak JS (2006) Application of wavelet transform technique to detect tool failure in turning operations. Int J Adv Manuf Technol 28:1078–1083. https://doi.org/10.1007/s00170-004-2476-x
Xiaoli L (1999) On-line detection of the breakage of small diameter drills using current signature wavelet transform. Int J Mach Tools Manuf 39:157–164. https://doi.org/10.1016/S0890-6955(97)00066-7
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232
Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 785–794
Chu W, Keerthi SS (2007) Support vector ordinal regression. Neural Comput 19(3):792–815
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958. https://doi.org/10.1021/ci034160g
Wei H, Peng C, Gao H, Wang X, Wang X (2019) On establishment and validation of a new predictive model for material removal in abrasive flow machining. Int J Mach Tools Manuf 138:66–79
He QW, Yang X, Wu XH, Qu XT, Zhao J (2017) Research on material removal of belt polishing for blade complex surface. In: Current Trends in Computer Science and Mechanical Automation Vol. 2. Sciendo Migration, pp 319–333
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This work was supported by the National Key Research and Development Program of China (No.2018YFC0310400) and Guangzhou Municipal Innovative Manufacturing Research Program, China, [NO.SD0500544].
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Gao, K., Chen, H., Zhang, X. et al. A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718. Int J Adv Manuf Technol 105, 217–232 (2019). https://doi.org/10.1007/s00170-019-04170-7
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DOI: https://doi.org/10.1007/s00170-019-04170-7