{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T23:13:22Z","timestamp":1768778002958,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies.<\/jats:p>","DOI":"10.3390\/s24113432","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T09:33:31Z","timestamp":1716802411000},"page":"3432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1059-1185","authenticated-orcid":false,"given":"Lorenzo","family":"Colantonio","sequence":"first","affiliation":[{"name":"Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, Research Institute for the Science and Management of Risks, University of Mons, 7000 Mons, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0442-8015","authenticated-orcid":false,"given":"Lucas","family":"Equeter","sequence":"additional","affiliation":[{"name":"Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, Research Institute for the Science and Management of Risks, University of Mons, 7000 Mons, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6218-4736","authenticated-orcid":false,"given":"Pierre","family":"Dehombreux","sequence":"additional","affiliation":[{"name":"Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, Research Institute for the Science and Management of Risks, University of Mons, 7000 Mons, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4299-2699","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Ducobu","sequence":"additional","affiliation":[{"name":"Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, Research Institute for the Science and Management of Risks, University of Mons, 7000 Mons, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.wear.2010.11.059","article-title":"An in-depth investigation of the cutting speed impact on the degraded microstructure of worn PCBN cutting tools","volume":"271","author":"Angseryd","year":"2011","journal-title":"Wear"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Klocke, F., and Kuchle, A. (2009). Manufacturing Processes, Springer.","DOI":"10.1007\/978-3-540-92259-9"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1007\/s00170-018-1683-9","article-title":"A mathematical model for the joint optimization of machining conditions and tool replacement policy with stochastic tool life in the milling process","volume":"96","author":"Zaretalab","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16878140211026720","DOI":"10.1177\/16878140211026720","article-title":"Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy","volume":"13","author":"Baig","year":"2021","journal-title":"Adv. Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kunto\u011flu, M., Aslan, A., Pimenov, D.Y., Usca, \u00dc.A., Salur, E., Gupta, M.K., Mikolajczyk, T., Giasin, K., Kap\u0142onek, W., and Sharma, S. (2020). A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors, 21.","DOI":"10.3390\/s21010108"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/S0890-6955(98)00028-5","article-title":"Optimization of cutting parameters for maximizing tool life","volume":"39","author":"Choudhury","year":"1999","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1007\/s001700200162","article-title":"Development of empirical models for surface roughness prediction in finish turning","volume":"20","author":"Wang","year":"2002","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1016\/j.wear.2004.03.010","article-title":"Experimental support for a model-based prediction of tool wear","volume":"257","author":"Wong","year":"2004","journal-title":"Wear"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Equeter, L., Ducobu, F., Rivi\u00e8re-Lorph\u00e8vre, E., Serra, R., and Dehombreux, P. (2020). An analytic approach to the Cox proportional hazards model for estimating the lifespan of cutting tools. J. Manuf. Mater. Process., 4.","DOI":"10.3390\/jmmp4010027"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s00170-020-06144-6","article-title":"Prediction of cutting tool wear during a turning process using artificial intelligence techniques","volume":"111","author":"Marani","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"122997","DOI":"10.1016\/j.eswa.2023.122997","article-title":"Targeted transfer learning through distribution barycenter medium for intelligent fault diagnosis of machines with data decentralization","volume":"244","author":"Yang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106164","DOI":"10.1016\/j.engfailanal.2022.106164","article-title":"Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion","volume":"136","author":"Gao","year":"2022","journal-title":"Eng. Fail. Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cja.2023.08.011","article-title":"Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review","volume":"37","author":"Wang","year":"2023","journal-title":"Chin. J. Aeronaut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.promfg.2020.05.134","article-title":"Digital image processing with deep learning for automated cutting tool wear detection","volume":"48","author":"Bergs","year":"2020","journal-title":"Procedia Manuf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s12541-020-00419-4","article-title":"An analysis of the focus variation microscope and its application in the measurement of tool parameter","volume":"21","author":"Yuan","year":"2020","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s00170-012-4177-1","article-title":"A review of flank wear prediction methods for tool condition monitoring in a turning process","volume":"65","author":"Siddhpura","year":"2013","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.21741\/9781644902479-137","article-title":"Comparison of cutting tool wear classification performance with artificial intelligence techniques","volume":"28","author":"Colantonio","year":"2023","journal-title":"Mater. Res. Proc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10845-020-01564-3","article-title":"Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data","volume":"32","author":"Brito","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Brili, N., Ficko, M., and Klan\u010dnik, S. (2021). Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process. Sensors, 21.","DOI":"10.3390\/s21051917"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s00170-020-06055-6","article-title":"Indirect cutting tool wear classification using deep learning and chip colour analysis","volume":"111","author":"Pagani","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ferrando Chac\u00f3n, J.L., Fern\u00e1ndez de Barrena, T., Garc\u00eda, A., S\u00e1ez de Buruaga, M., Badiola, X., and Vicente, J. (2021). A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals. Sensors, 21.","DOI":"10.3390\/s21175984"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11740-020-00989-2","article-title":"Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms","volume":"14","author":"Segreto","year":"2020","journal-title":"Prod. Eng. Res. Devel."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Colantonio, L., Equeter, L., Dehombreux, P., and Ducobu, F. (2021). A systematic literature review of cutting tool wear monitoring in turning by using artificial intelligence techniques. Machines, 9.","DOI":"10.3390\/machines9120351"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jmapro.2017.11.014","article-title":"In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm","volume":"31","author":"Pandiyan","year":"2018","journal-title":"J. Manuf. Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jmsy.2024.01.009","article-title":"A novel exponential model for tool remaining useful life prediction","volume":"73","author":"Sun","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111120","DOI":"10.1016\/j.ymssp.2024.111120","article-title":"A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities","volume":"209","author":"Li","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110922","DOI":"10.1016\/j.asoc.2023.110922","article-title":"Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy","volume":"148","author":"Kim","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TNN.2011.2162110","article-title":"Comprehensive review of neural network-based prediction intervals and new advances","volume":"22","author":"Khosravi","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TNS.2006.871662","article-title":"A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes","volume":"53","author":"Zio","year":"2006","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_30","unstructured":"(2022, October 03). ISO 3685\u2014Tool Life Testing with Single-Point Turning Tools. Available online: https:\/\/www.iso.org\/fr\/standard\/9151.html."},{"key":"ref_31","unstructured":"Seco Tools (2018). Turning Catalog and Technical Guide, Seco Tools AB. [2nd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.jmsy.2021.12.002","article-title":"Intelligent tool wear monitoring and multi-step prediction based on deep learning model","volume":"62","author":"Cheng","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.precisioneng.2021.07.019","article-title":"Identification of tool wear using acoustic emission signal and machine learning methods","volume":"72","author":"Twardowski","year":"2021","journal-title":"Precis. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","article-title":"The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms","volume":"15","author":"Welch","year":"1967","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1037\/a0028087","article-title":"Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches","volume":"17","author":"Bishara","year":"2012","journal-title":"Psychol. Methods"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1080\/03610910903289151","article-title":"Comparing Pearson correlations: Dealing with heteroscedasticity and nonnormality","volume":"38","author":"Wilcox","year":"2009","journal-title":"Commun. Stat.-Simul. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ymssp.2010.07.014","article-title":"Tool wear monitoring by machine learning techniques and singular spectrum analysis","volume":"25","author":"Kilundu","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1687814017750434","DOI":"10.1177\/1687814017750434","article-title":"The relationships between cutting parameters, tool wear, cutting force and vibration","volume":"10","author":"Xu","year":"2018","journal-title":"Adv. Mech. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0890-6955(89)90001-1","article-title":"On the correlation between dynamic cutting force and tool wear","volume":"29","author":"Lee","year":"1989","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","article-title":"A systematic review on overfitting control in shallow and deep neural networks","volume":"54","author":"Bejani","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_41","unstructured":"(2024, May 24). Keras. Open Source API. Available online: https:\/\/keras.io\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhydrol.2009.12.013","article-title":"Uncertainty assessment and ensemble flood forecasting using bootstrap-based artificial neural networks (BANNs)","volume":"382","author":"Tiwari","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chambers, J.M., Cleveland, W.S., Kleiner, B., and Tukey, P.A. (2018). Graphical Methods for Data Analysis, Chapman and Hall\/CRC.","DOI":"10.1201\/9781351072304"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3432\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:49:04Z","timestamp":1760107744000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,26]]},"references-count":43,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113432"],"URL":"https:\/\/doi.org\/10.3390\/s24113432","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,26]]}}}