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Kunto\u011flu, A. Aslan, D.Y. Pimenov, \u00dc.A. Usca, E. Salur, M.K. Gupta, T. Mikolajczyk, K. Giasin, W. Kap\u0142onek, and S. Sharma, \u201cA review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends,\u201d Sensors, vol.21, no.1, 108, Jan. 2021. 10.3390\/s21010108","DOI":"10.3390\/s21010108"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] M. Kunto\u011flu and H. Sa\u011flam, \u201cInvestigation of progressive tool wear for determining of optimized machining parameters in turning,\u201d Measurement, vol.140, pp.427-436, 2019. 10.1016\/j.measurement.2019.04.022","DOI":"10.1016\/j.measurement.2019.04.022"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] Y. Zhou and W. Xue, \u201cReview of tool condition monitoring methods in milling processes,\u201d Int. J. Adv. Manuf. Technol., vol.96, no.5-8, pp.2509-2523, 2018. 10.1007\/s00170-018-1768-5","DOI":"10.1007\/s00170-018-1768-5"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Y. Li, C. Liu, J. Hua, J. Gao, and P. Maropoulos, \u201cA novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning,\u201d CIRP Annals, vol.68, no.1, pp.487-490, 2019. 10.1016\/j.cirp.2019.03.010","DOI":"10.1016\/j.cirp.2019.03.010"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] D. Zhu, X. Zhang, and H. Ding, \u201cTool wear characteristics in machining of nickel-based superalloys,\u201d Int. J. Mach. Tools Manuf., vol.64, pp.60-77, 2013. 10.1016\/j.ijmachtools.2012.08.001","DOI":"10.1016\/j.ijmachtools.2012.08.001"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] C. Liu, Y. Li, J. Hua, N. Lu, and W. Mou, \u201cReal-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts,\u201d Int. J. Adv. Manuf. Technol., vol.97, no.1-4, pp.229-241, 2018. 10.1007\/s00170-018-1916-y","DOI":"10.1007\/s00170-018-1916-y"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] K. Salonitis and A. Kolios, \u201cReliability assessment of cutting tool life based on surrogate approximation methods,\u201d Int. J. Adv. Manuf. Technol., vol.71, no.5-8, pp.1197-1208, 2014. 10.1007\/s00170-013-5560-2","DOI":"10.1007\/s00170-013-5560-2"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] A.G. Rehorn, J. Jiang, P.E. Orban, and E.V. Bordatchev, \u201cState-of-the-art methods and results in tool condition monitoring: A review,\u201d Int. J. Adv. Manuf. Technol., vol.26, no.7-8, pp.693-710, 2005.","DOI":"10.1007\/s00170-004-2038-2"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Swain, I. Panigrahi, A.K. Sahoo, and A. Panda, \u201cAdaptive tool condition monitoring system: A brief review,\u201d Proc. International Conference on Recent Advances in Materials, Manufacturing and Energy Systems (ICRAMMES), Vijayawada, India, pp.474-478, Jan. 2019. 10.1016\/j.matpr.2019.05.386","DOI":"10.1016\/j.matpr.2019.05.386"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] D. An, N.H. Kim, and J.-H. Choi, \u201cPractical options for selecting data-driven or physics-based prognostics algorithms with reviews,\u201d Reliab. Eng. Syst. Saf., vol.133, pp.223-236, 2015. 10.1016\/j.ress.2014.09.014","DOI":"10.1016\/j.ress.2014.09.014"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] X. Cao, B. Chen, and N. Zeng, \u201cA deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis,\u201d Neurocomputing, vol.409, pp.173-190, Oct. 2020. 10.1016\/j.neucom.2020.05.064","DOI":"10.1016\/j.neucom.2020.05.064"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] J. Jiao, M. Zhao, J. Lin, and C. Ding, \u201cDeep coupled dense convolutional network with complementary data for intelligent fault diagnosis,\u201d IEEE Trans. Ind. Electron., vol.66, no.12, pp.9858-9867, 2019. 10.1109\/tie.2019.2902817","DOI":"10.1109\/TIE.2019.2902817"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] L. Wen, X. Li, L. Gao, and Y. Zhang, \u201cA new convolutional neural network-based data-driven fault diagnosis method,\u201d IEEE Trans. Ind. Electron., vol.65, no.7, pp.5990-5998, 2018. 10.1109\/tie.2017.2774777","DOI":"10.1109\/TIE.2017.2774777"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] X.-C. Cao, B.-Q. Chen, B. Yao, and W.-P. He, \u201cCombining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification,\u201d Comput. Ind., vol.106, pp.71-84, 2019. 10.1016\/j.compind.2018.12.018","DOI":"10.1016\/j.compind.2018.12.018"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] F. Aghazadeh, A. Tahan, and M. Thomas, \u201cTool condition monitoring using spectral subtraction and convolutional neural networks in milling process,\u201d Int. J. Adv. Manuf. Technol., vol.98, no.9-12, pp.3217-3227, 2018. 10.1007\/s00170-018-2420-0","DOI":"10.1007\/s00170-018-2420-0"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] H. Oo, W. Wang, and Z. Liu, \u201cTool wear monitoring system in belt grinding based on image-processing techniques,\u201d Int. J. Adv. Manuf. Technol., vol.111, no.7-8, pp.2215-2229, Dec. 2020. 10.1007\/s00170-020-06254-1","DOI":"10.1007\/s00170-020-06254-1"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] C. Zhou, K. Guo, and J. Sun, \u201cAn integrated wireless vibration sensing tool holder for milling tool condition monitoring with singularity analysis,\u201d Measurement, vol.174, 109038, April 2021. 10.1016\/j.measurement.2021.109038","DOI":"10.1016\/j.measurement.2021.109038"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M. Hassan, A. Sadek, and M.H. Attia, \u201cNovel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications,\u201d CIRP Annals, vol.70, no.1, pp.87-90, 2021. 10.1016\/j.cirp.2021.03.024","DOI":"10.1016\/j.cirp.2021.03.024"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Yuan, L. Liu, Z. Yang, and Y. Zhang, \u201cTool wear condition monitoring by combining variational mode decomposition and ensemble learning,\u201d Sensors, vol.20, no.21, 6113, Nov. 2020. 10.3390\/s20216113","DOI":"10.3390\/s20216113"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] Y. Shen, F. Yang, M.S. Habibullah, J. Ahmed, A.K. Das, Y. Zhou, and C.L. Ho, \u201cPredicting tool wear size across multi-cutting conditions using advanced machine learning techniques,\u201d J. Intell. Manuf., vol.32, no.6, pp.1753-1766, Aug. 2021. 10.1007\/s10845-020-01625-7","DOI":"10.1007\/s10845-020-01625-7"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] A. Siddhpura and R. Paurobally, \u201cA review of flank wear prediction methods for tool condition monitoring in a turning process,\u201d Int. J. Adv. Manuf. Technol., vol.65, no.1-4, pp.371-393, 2013. 10.1007\/s00170-012-4177-1","DOI":"10.1007\/s00170-012-4177-1"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] C. Zhou, K. Guo, and J. Sun, \u201cSound singularity analysis for milling tool condition monitoring towards sustainable manufacturing,\u201d Mech. Syst. Signal Proc., vol.157, 107738, 2021. 10.1016\/j.ymssp.2021.107738","DOI":"10.1016\/j.ymssp.2021.107738"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] A. Jim\u00e9nez, M. Arizmendi, and J.M. S\u00e1nchez, \u201cExtraction of tool wear indicators in peck-drilling of inconel 718,\u201d Int. J. Adv. Manuf. Technol., vol.114, no.9-10, pp.2711-2720, 2021. 10.1007\/s00170-021-07058-7","DOI":"10.1007\/s00170-021-07058-7"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] X.-R. Li, J.-M. Zhu, F.-Q. Tian, and H.-F. Pan, \u201cDiscrimination and prediction of tool wear state based on gray theory,\u201d J. Test. Eval., vol.48, no.6, pp.4262-4282, Nov. 2020. 10.1520\/jte20180302","DOI":"10.1520\/JTE20180302"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] N. Chen, B. Hao, Y. Guo, L. Li, and N. He, \u201cResearch on tool wear monitoring in drilling process based on APSO-LS-SVM approach,\u201d Int. J. Adv. Manuf. Technol., vol.108, no.1-4, pp.2091-2101, 2020. 10.1007\/s00170-020-05549-7","DOI":"10.1007\/s00170-020-05549-7"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] K.A. Ajayram, R. Jegadeeshwaran, G. Sakthivel, R. Sivakumar, and A.D. Patange, \u201cCondition monitoring of carbide and non-carbide coated tool insert using decision tree and random tree \u2014 A statistical learning,\u201d Proc. 28th International Conference on Processing and Fabrication of Advanced Materials (PFAM), Chennai, INDIA, pp.1201-1209, Dec. 2021. 10.1016\/j.matpr.2021.02.065","DOI":"10.1016\/j.matpr.2021.02.065"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] J. Ou, H. Li, G. Huang, and G. Yang, \u201cIntelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine,\u201d Measurement, vol.167, 108153, July 2020.","DOI":"10.1016\/j.measurement.2020.108153"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] H. Mamledesai, M.A. Soriano, and R. Ahmad, \u201cA qualitative tool condition monitoring framework using convolution neural network and transfer learning,\u201d Appl. Sci., vol.10, no.20, 7298, Oct. 2020. 10.3390\/app10207298","DOI":"10.3390\/app10207298"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] H. Xu, G.S. Hong, J.H. Zhou, J. Hong, and K.S. Woon, \u201cCoarse-to-fine tool condition monitoring using multiple gated recurrent units,\u201d Proc. 45th Annual Conference of the IEEE Industrial Electronics Society (IECON), Lisbon, Portugal, pp.3737-3742, Oct. 2019. 10.1109\/iecon.2019.8927157","DOI":"10.1109\/IECON.2019.8927157"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] W. Cai, W. Zhang, X. Hu, and Y. Liu, \u201cA hybrid information model based on long short-term memory network for tool condition monitoring,\u201d J. Intell. Manuf., vol.31, no.9-12, pp.1497-1510, 2020. 10.1007\/s10845-019-01526-4","DOI":"10.1007\/s10845-019-01526-4"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] B. Chen, Z. Zhang, Y. Zi, and Z. He, \u201cNovel ensemble analytic discrete framelet expansion for machinery fault diagnosis,\u201d J. Mech. Eng., vol.50, no.17, pp.77-86, 2014. 10.3901\/jme.2014.17.077","DOI":"10.3901\/JME.2014.17.077"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] R. Rubinstein, \u201cThe cross-entropy method for combinatorial and continuous optimization,\u201d Methodol. Comput. Appl. Probab., vol.1, no.2, pp.127-190, 1999. 10.1023\/A:1010091220143","DOI":"10.1023\/A:1010091220143"},{"key":"33","unstructured":"[33] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014. 10.48550\/arXiv.1412.6980"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] J. Duan, J. Duan, H. Zhou, X. Zhan, T. Li, and T. Shi, \u201cMulti-frequency-band deep CNN model for tool wear prediction,\u201d Meas. Sci. Technol., vol.32, no.6, 065009, June 2021. 10.1088\/1361-6501\/abb7a0","DOI":"10.1088\/1361-6501\/abb7a0"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] Y.F. Zeng, R.L. Liu, and X.F. Liu, \u201cA novel approach to tool condition monitoring based on multi-sensor data fusion imaging and an attention mechanism,\u201d Meas. Sci. Technol., vol.32, no.5, 055601, May 2021. 10.1088\/1361-6501\/abea3f","DOI":"10.1088\/1361-6501\/abea3f"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] X. Zhang, X. Lu, W. Li, and S. Wang, \u201cPrediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM,\u201d Int. J. Adv. Manuf. 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