Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms

J. Ahire, Artificial Neural Networks: The Brain Behind AI, Independently Published, 2018.

Ahmad, 2015, Development of tool wear machining monitoring using novel statistical analysis method, I-kaz™, Procedia Eng., 101, 355, 10.1016/j.proeng.2015.02.043

Altan, 2020, Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances, Mech. Syst. Signal Process., 138, 106548, 10.1016/j.ymssp.2019.106548

Altan, 2019, The effect of kernel values in support vector machine to forecasting performance of financial time series, J. Cogn. Syst., 4, 17

Benkedjouh, 2018, Tool wear condition monitoring based on continuous wavelet transform and blind source separation, Int. J. Adv. Manuf. Technol., 97, 3311, 10.1007/s00170-018-2018-6

Bhat, 2016, Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images, Measurement, 90, 500, 10.1016/j.measurement.2016.05.022

Bhuiyan, 2016, Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring, Measurement, 92, 208, 10.1016/j.measurement.2016.06.006

Cai, 2020, A hybrid information model based on long short-term memory network for tool condition monitoring, J. Intell. Manuf., 1

Cuka, 2017, Fuzzy logic based tool condition monitoring for end-milling, Rob. Comput. Integr. Manuf., 47, 22, 10.1016/j.rcim.2016.12.009

A. Echelard, J.L. Véhel, Local Regularity Preserving Signal Denoising I: Hölder Exponents, 2013.

Elangovan, 2011, Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm, Expert Syst. Appl., 38, 4450, 10.1016/j.eswa.2010.09.116

Elangovan, 2011, Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool, Expert Syst. Appl., 38, 15202, 10.1016/j.eswa.2011.05.081

Fang, 2014, A method of using Hoelder exponents to monitor tool-edge wear in high-speed finish machining, Int. J. Adv. Manuf. Technol., 72, 1593, 10.1007/s00170-014-5764-0

A.A.A.K. Goebel, BEST lab, UC Berkeley. Milling Data Set, in: N.A.P.D. Repository (ed.). NASA Ames Research Center, Moffett Field, CA.

H. Guo, G. Xiong, F. Xi, J. Zheng, X. Ping, Detection and compensation of cutting tool wear based on Lipschitz exponent, in: Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005, 2005, IEEE, pp. 1662–1667.

Guo, 2020, Tool condition monitoring in milling using a force singularity analysis approach, Int. J. Adv. Manuf. Technol., 1

R. Gupta, V. Anil Kumar, U. Gururaja, B. Shivaram, Y. Maruti Prasad, P. Ramkumar, K. Chakravarthi, P. Sarkar, Processing and characterization of Inconel 625 Nickel base Superalloy, Materials Science Forum, 2015. Trans Tech Publ, pp. 38–40.

R. Hacioğlu, Prediction of solar radiation based on machine learning methods, J. Cogn. Syst., 2, 16–20.

Huang, 2015, A PNN self-learning tool breakage detection system in end milling operations, Appl. Soft Comput., 37, 114, 10.1016/j.asoc.2015.08.019

Jiang, 2018, Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network, Meas. Sci. Technol., 29, 10.1088/1361-6501/aab945

Junior, 2018, Dressing tool condition monitoring through impedance-based sensors: part 2—neural networks and k-nearest neighbor classifier approach, Sensors, 18, 4453, 10.3390/s18124453

S. Karasu, A. Altan, Recognition Model for Solar Radiation Time Series based on Random Forest with Feature Selection Approach, in: 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), 2019, IEEE, pp. 8–11.

S. Karasu, A. Altan, Z. Saraç, R. Hacioğlu, Prediction of Bitcoin prices with machine learning methods using time series data, in: 2018 26th Signal Processing and Communications Applications Conference (SIU), 2-5 May 2018, 2018, pp. 1–4.

Kilundu, 2011, Tool wear monitoring by machine learning techniques and singular spectrum analysis, Mech. Syst. Sig. Process., 25, 400, 10.1016/j.ymssp.2010.07.014

Krishnakumar, 2015, Tool wear condition prediction using vibration signals in high speed machining (HSM) of titanium (Ti-6Al-4 V) alloy, Procedia Comput. Sci., 50, 270, 10.1016/j.procs.2015.04.049

Kunpeng, 2012, Signal processing for tool condition monitoring: from wavelet analysis to sparse decomposition

Lamraoui, 2015, Chatter detection in milling machines by neural network classification and feature selection, J. Vib. Control, 21, 1251, 10.1177/1077546313493919

Lee, 2017, Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation, J. Mech. Sci. Technol., 31, 5695, 10.1007/s12206-017-1110-4

Li, 2005, Multi-scale statistical process monitoring in machining, IEEE Trans. Ind. Electron., 52, 924, 10.1109/TIE.2005.847580

Liao, 2007, A wavelet-based methodology for grinding wheel condition monitoring, Int. J. Mach. Tools Manuf, 47, 580, 10.1016/j.ijmachtools.2006.05.008

Lu, 2019, In-process complex machining condition monitoring based on deep forest and process information fusion, Int. J. Adv. Manuf. Technol., 104, 1953, 10.1007/s00170-019-03919-4

Mallat, 1999

Mohanraj, 2019, Tool condition monitoring in the milling process with vegetable based cutting fluids using vibration signatures, Mater. Test., 61, 282, 10.3139/120.111318

Mohanraj, 2020, Tool condition monitoring techniques in milling process—a review, J. Mater. Res. Technol., 9, 1032, 10.1016/j.jmrt.2019.10.031

Ong, 2019, Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision, Int. J. Adv. Manuf. Technol., 104, 1369, 10.1007/s00170-019-04020-6

Painuli, 2014, Tool condition monitoring using K-star algorithm, Expert Syst. Appl., 41, 2638, 10.1016/j.eswa.2013.11.005

Patange, 2019, Milling cutter condition monitoring using machine learning approach, IOP Conf. Ser.: Mater. Sci. Eng., 624, 10.1088/1757-899X/624/1/012030

Rmili, 2016, An automatic system based on vibratory analysis for cutting tool wear monitoring, Measurement, 77, 117, 10.1016/j.measurement.2015.09.010

Serin, 2020, Review of tool condition monitoring in machining and opportunities for deep learning, Int. J. Adv. Manuf. Technol., 1

Shankar, 2019, Tool condition monitoring while using vegetable based cutting fluids during milling of inconel 625, J. Adv. Manuf. Syst., 18, 563, 10.1142/S0219686719500306

Shankar, 2019, Prediction of cutting tool wear during milling process using artificial intelligence techniques, Int. J. Comput. Integr. Manuf., 32, 174, 10.1080/0951192X.2018.1550681

C. Stolojescu, I. Railean, S. Moga, A. Isar, Comparison of wavelet families with application to WiMAX traffic forecasting, in: 2010 12th international conference on optimization of electrical and electronic equipment, 2010, IEEE, pp. 932–937.

Sun, 2020, In-process tool condition forecasting based on a deep learning method, Rob. Comput. Integr. Manuf., 64, 10.1016/j.rcim.2019.101924

Tao, 2019, A novel method for tool condition monitoring based on long short-term memory and hidden Markov model hybrid framework in high-speed milling Ti-6Al-4V, Int. J. Adv. Manuf. Technol., 105, 3165, 10.1007/s00170-019-04464-w

Thangarasu, 2020, Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network, Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 234, 329, 10.1177/0954406219873932

K.K. Ton, K. Worden, An Application of Holder Exponents to Condition Monitoring, sem-proceedings.com.

Uekita, 2017, Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain, Measurement, 103, 199, 10.1016/j.measurement.2017.02.035

Wang, 2014, Force sensor based tool condition monitoring using a heterogeneous ensemble learning model, Sensors, 14, 21588, 10.3390/s141121588

Wong, 2020, Technical data-driven tool condition monitoring challenges for CNC milling: a review, Int. J. Adv. Manuf. Technol., 107, 4837, 10.1007/s00170-020-05303-z

Zhou, 2019, Singularity analysis of cutting force and vibration for tool condition monitoring in milling, IEEE Access, 7, 134113, 10.1109/ACCESS.2019.2941287

Zhou, 2020, The monitoring of milling tool tipping by estimating holder exponents of vibration, IEEE Access, 8, 96661, 10.1109/ACCESS.2020.2996607

Zhou, 2020, Tool condition monitoring in milling using a force singularity analysis approach, Int. J. Adv. Manuf. Technol., 107, 1785, 10.1007/s00170-019-04664-4

Zhou, 2020, Vibration singularity analysis for milling tool condition monitoring, Int. J. Mech. Sci., 166, 10.1016/j.ijmecsci.2019.105254

Zhu, 2009, Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results, Int. J. Machine Tools Manuf., 49, 537, 10.1016/j.ijmachtools.2009.02.003