{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T10:57:26Z","timestamp":1778065046636,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"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>This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.<\/jats:p>","DOI":"10.3390\/s22103807","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T08:34:29Z","timestamp":1652776469000},"page":"3807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7400-0033","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Sio-Sever","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Instrumentaci\u00f3n y Ac\u00fastica Aplicada, Departamento de Ingenier\u00eda Mec\u00e1nica, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-8707","authenticated-orcid":false,"given":"Juan Manuel","family":"Lopez","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Instrumentaci\u00f3n y Ac\u00fastica Aplicada, Departamento de Telem\u00e1tica y Electr\u00f3nica, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9sar","family":"Asensio-Rivera","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Instrumentaci\u00f3n y Ac\u00fastica Aplicada, Departamento de Teoria de la Se\u00f1al y Comunicaciones, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Vizan-Idoipe","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Mec\u00e1nica, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1699-7389","authenticated-orcid":false,"given":"Guillermo","family":"de Arcas","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Instrumentaci\u00f3n y Ac\u00fastica Aplicada, Departamento de Ingenier\u00eda Mec\u00e1nica, Universidad Polit\u00e9cnica de Madrid, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s00170-015-7418-2","article-title":"The effect of variable cutting depth and thickness on milling stability for orthogonal turn-milling","volume":"82","author":"Yan","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1007\/s00170-018-2127-2","article-title":"A force-measuring-based approach for feed rate optimization considering the stochasticity of machining allowance","volume":"97","author":"Zhang","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.measurement.2018.06.018","article-title":"Accuracy of a new online method for measuring machining parameters in milling","volume":"128","author":"Diez","year":"2018","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s00170-010-2711-6","article-title":"The development of an end-milling process depth of cut monitoring system","volume":"52","author":"Prickett","year":"2010","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.ymssp.2016.06.027","article-title":"Characterization of tool-workpiece contact during the micromachining of conductive materials","volume":"83","author":"Haber","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.sna.2015.05.015","article-title":"Conductance sensing for monitoring micromechanical machining of conductive materials","volume":"232","author":"Toro","year":"2015","journal-title":"Sens. Actuators A Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00170-020-05751-7","article-title":"Detection of tool breakage during milling process through acoustic emission","volume":"109","author":"Sun","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107478","DOI":"10.1016\/j.measurement.2020.107478","article-title":"Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining","volume":"154","author":"Ahmed","year":"2020","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_9","first-page":"2419","article-title":"Investigation of milling stability under cutting fluid supply by microphone signal analysis","volume":"30","author":"Lee","year":"2018","journal-title":"Sens. Mater."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s40430-018-0989-8","article-title":"Estimation of stable cutting zone in turning based on empirical mode decomposition and statistical approach","volume":"40","author":"Shrivastava","year":"2018","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_11","first-page":"3575","article-title":"Implementation of an Add-on Device that Monitors the Sound of a Machine Tool and Automatically Suppresses Chatter","volume":"31","author":"Lee","year":"2019","journal-title":"Sens. Mater."},{"key":"ref_12","first-page":"106840","article-title":"Recent progress of chatter prediction, detection and suppression in milling","volume":"143","author":"Zhu","year":"2020","journal-title":"Mech. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, P.L., and Tsai, Y.T. (2018). Numerical Analysis of CNC Milling Chatter Using Embedded Miniature MEMS microphone Array System. Inventions, 3.","DOI":"10.3390\/inventions3010005"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sio-Sever, A., Leal-Mu\u00f1oz, E., Lopez-Navarro, J., Alzugaray-Franz, R., Vizan-Idoipe, A., and de Arcas-Castro, G. (2020). Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission. Sensors, 20.","DOI":"10.3390\/s20185326"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111012","DOI":"10.1115\/1.4040617","article-title":"On-Line Energy-Based Milling Chatter Detection","volume":"140","author":"Caliskan","year":"2018","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_16","first-page":"873","article-title":"Acoustic Signal Analysis by Teager\u2013Huang Transform for Milling Chatter Recognition","volume":"32","author":"Lee","year":"2020","journal-title":"Sens. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s00170-018-2306-1","article-title":"Chatter detection and stability region acquisition in thin-walled workpiece milling based on CMWT","volume":"98","author":"Gao","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3361","DOI":"10.1007\/s00170-019-03686-2","article-title":"Tool wear monitoring and prediction based on sound signal","volume":"103","author":"Liu","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s00170-020-06144-6","article-title":"Prediction of cutting tool wear during milling process using artificial intelligence techniques","volume":"111","author":"Marani","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1007\/s12206-017-0606-2","article-title":"A neural network-based approach for background noise reduction in airborne acoustic emission of a machining process","volume":"31","author":"Zafar","year":"2017","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1177\/1475921720903442","article-title":"A comparative study of two indirect methods to monitor surface integrity of ground components","volume":"19","author":"Duarte","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mirifar, S., Kadivar, M., and Azarhoushang, B. (2020). First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors. J. Manuf. Mater. Process., 4.","DOI":"10.3390\/jmmp4020035"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ifacol.2019.11.172","article-title":"Machine Learning Framework for Predictive Maintenance in Milling","volume":"52","author":"Traini","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compind.2018.12.018","article-title":"Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification","volume":"106","author":"Cao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wu, X., Liu, Y., Zhou, X., and Mou, A. (2019). Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process. Sensors, 19.","DOI":"10.3390\/s19183817"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"03003","DOI":"10.1051\/matecconf\/201821703003","article-title":"Deep Neural Network Tool Chatter Model for Aluminum Surface Milling Using Acoustic Emission Sensor","volume":"217","author":"Hasan","year":"2018","journal-title":"MATEC Web Conf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s00170-020-05931-5","article-title":"Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine","volume":"110","author":"Guo","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00170-019-03906-9","article-title":"Tool wear state recognition based on GWO\u2013SVM with feature selection of genetic algorithm","volume":"104","author":"Liao","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s00170-016-9706-x","article-title":"Tool condition monitoring and degradation estimation in rotor slot machining process","volume":"91","author":"Liu","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.15376\/biores.14.2.3379-3388","article-title":"Use of cutting force and vibro-acoustic signals in tool wear monitoring based on multiple regression technique for compreg milling","volume":"14","author":"Szymanowski","year":"2019","journal-title":"Bioresources"},{"key":"ref_32","first-page":"16","article-title":"Prediction models for on-line cutting tool and machined surface condition monitoring during hard turning considering vibration signal","volume":"520","author":"Panda","year":"2020","journal-title":"Mech. Ind."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107370","DOI":"10.1016\/j.measurement.2019.107370","article-title":"Kriging versus Bezier and regression methods for modeling and prediction of cutting force and surface roughness during high speed edge trimming of Carbon fiber reinforced polymers","volume":"152","author":"Slamani","year":"2019","journal-title":"Measurement"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vaseghi, S.V. (2007). Advanced Signal Processing and Digital Noise Reduction, Springer.","DOI":"10.1002\/9780470740156"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3217","DOI":"10.1007\/s00170-018-2420-0","article-title":"Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process","volume":"98","author":"Aghazadeh","year":"2018","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_36","first-page":"30","article-title":"Tool condition monitoring using spectral subtraction algorithm and artificial intelligence methods in milling process","volume":"7","author":"Aghazadeh","year":"2018","journal-title":"Int. J. Mech. Eng. Robot. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hyvarinen, A., Oja, E., and Karhunen, J. (2001). Independent Component Analysis, Wiley.","DOI":"10.1002\/0471221317"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/97.763148","article-title":"Gaussian Moments for Noisy Independent Component Analysis","volume":"6","year":"1999","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_39","unstructured":"Oppenheim, A.V. (1999). Discrete-Time Signal Processing, Prentice Hall."},{"key":"ref_40","unstructured":"Sever, A.S., Leal-Mu\u00f1oz, E., Idoipe, A.V., Navarro, J.M.L., and de Arcas Castro, G. (2019, January 16\u201319). Use of the phenomenon of acoustic emission for real-time monitoring of milling processes. Proceedings of the INTER-NOISE 2019 MADRID\u201448th International Congress and Exhibition on Noise Control, Madrid, Spain."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, F.F., and Cox, T.J. (2019). Digital Signal Processing in Audio and Acoustical Engineering, CRC Press.","DOI":"10.1201\/9781315117881"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Rasmussen, Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"McLachlan, G.J., Do, K.-A., and Ambroise, C. (2004). Analyzing Microarray Gene Expression Data, Wiley.","DOI":"10.1002\/047172842X"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3807\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:13:35Z","timestamp":1760138015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,17]]},"references-count":43,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103807"],"URL":"https:\/\/doi.org\/10.3390\/s22103807","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,17]]}}}