{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:25:17Z","timestamp":1774031117206,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T00:00:00Z","timestamp":1597104000000},"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>Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system\u2019s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.<\/jats:p>","DOI":"10.3390\/s20164493","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T09:28:57Z","timestamp":1597138137000},"page":"4493","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7929-0367","authenticated-orcid":false,"given":"Rui","family":"Silva","sequence":"first","affiliation":[{"name":"COMEGI, Campus de Vila Nova de Famalic\u00e3o, Universidade Lus\u00edada\u2013Norte, Edif\u00edcio da Lapa-Largo Tinoco de Sousa, 4760-108 Vila Nova de Famalic\u00e3o, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2879-1225","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"COMEGI, Campus de Vila Nova de Famalic\u00e3o, Universidade Lus\u00edada\u2013Norte, Edif\u00edcio da Lapa-Largo Tinoco de Sousa, 4760-108 Vila Nova de Famalic\u00e3o, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,11]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/S0890-6955(99)00122-4","article-title":"Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods","volume":"40","author":"Dimla","year":"2000","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ymssp.2017.11.022","article-title":"Predicting tool life in turning operations using neural networks and image processing","volume":"104","author":"Nowicki","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.infrared.2016.12.023","article-title":"Condition monitoring of turning process using infrared thermography technique\u2013An experimental approach","volume":"81","author":"Prasad","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0952-1976(00)00008-7","article-title":"Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network","volume":"13","author":"Kuo","year":"2000","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ymssp.2015.04.019","article-title":"Multi-sensor data fusion framework for CNC machining monitoring","volume":"66\u201367","author":"Duro","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1006\/mssp.1997.0123","article-title":"Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors","volume":"12","author":"Silva","year":"1998","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1006\/mssp.1999.1286","article-title":"The Adaptability of a Tool Wear Monitoring System Under Changing Cutting Conditions","volume":"14","author":"Silva","year":"2000","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S0890060417000518","article-title":"Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics","volume":"33","author":"Silva","year":"2018","journal-title":"Artif. Intell. Eng. Des. Anal. Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3419","DOI":"10.1016\/j.matpr.2015.07.317","article-title":"Tool condition monitoring system: A review","volume":"2","author":"Ambhore","year":"2015","journal-title":"Mater. Today Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S0952-1976(02)00004-0","article-title":"Tool condition monitoring using artificial intelligence methods","volume":"15","author":"Balazinski","year":"2002","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1006\/mssp.2001.1460","article-title":"On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research","volume":"16","author":"Sick","year":"2002","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0959-1524(00)00027-5","article-title":"Industrial application of neural networks\u2014An investigation","volume":"11","author":"Lennox","year":"2001","journal-title":"J. Process Control."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.precisioneng.2016.12.011","article-title":"Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals","volume":"48","author":"Patra","year":"2017","journal-title":"Precis. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.asoc.2015.02.037","article-title":"Tool wear assessment based on type-2 fuzzy uncertainty estimation on acoustic emission","volume":"31","author":"Ren","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.ymssp.2008.02.010","article-title":"Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system","volume":"23","author":"Aliustaoglu","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/S0890-6955(00)00112-7","article-title":"Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)","volume":"41","author":"Ertunc","year":"2001","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.precisioneng.2013.06.006","article-title":"Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach","volume":"38","author":"Karandikar","year":"2014","journal-title":"Precis. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.measurement.2016.05.022","article-title":"Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images, Meas","volume":"90","author":"Bhat","year":"2016","journal-title":"J. Int. Meas. Confed."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.eswa.2006.09.029","article-title":"Expert system development for vibration analysis in machine condition monitoring","volume":"34","author":"Ebersbach","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.renene.2016.12.095","article-title":"Machine learning methods for solar radiation forecasting: A review","volume":"105","author":"Voyant","year":"2017","journal-title":"Renew. Energy."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","article-title":"A survey of randomized algorithms for training neural networks","volume":"364\u2013365","author":"Zhang","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_23","first-page":"993","article-title":"Recurrent Neural Networks","volume":"19994575","author":"Anderson","year":"1999","journal-title":"IEEE Trans. Neural Networks."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/S0893-6080(00)00048-4","article-title":"Multi-step-ahead prediction using dynamic recurrent neural networks","volume":"13","author":"Parlos","year":"2002","journal-title":"Neural Netw."},{"key":"ref_25","first-page":"190","article-title":"Training and Analyzing Deep Recurrent Neural Networks","volume":"26","author":"Hermans","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech Recognition with Deep Recurrent Neural Networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_27","first-page":"318","article-title":"Learning internal representations by error propagation","volume":"1","author":"Rumelhart","year":"1986","journal-title":"Calif. Univ San Diego La Jolla Inst Cogn. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/0025-5564(74)90031-5","article-title":"The existence of persistent states in the brain","volume":"19","author":"Little","year":"1974","journal-title":"Math. Biosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","article-title":"Neural networks and physical systems with emergent collective computational abilities","volume":"79","author":"Hopfield","year":"1982","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_30","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2012). On the difficulty of training recurrent neural networks. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning Long-Term Dependencies with Gradient Descent is Difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_33","unstructured":"Martens, J., and Sutskever, I. (July, January 28). Learning recurrent neural networks with Hessian-free optimization. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short term memory. Neural computation","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Mem. Neural Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.engappai.2009.09.004","article-title":"Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring","volume":"23","author":"Warren","year":"2010","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4493\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:59:14Z","timestamp":1760176754000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/16\/4493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,11]]},"references-count":35,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20164493"],"URL":"https:\/\/doi.org\/10.3390\/s20164493","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,11]]}}}