{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T21:07:28Z","timestamp":1767992848979,"version":"3.49.0"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation program","award":["958205"],"award-info":[{"award-number":["958205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vibrations are a common issue in the machining and metal-cutting sector, in which the spindle vibration is primarily responsible for the poor surface quality of workpieces. The consequences range from the need to manually finish the metal surfaces, resulting in time-consuming and costly operations, to high scrap rates, with the corresponding waste of time and resources. The main problem of conventional solutions is that they address the suppression of machine vibrations separately from the quality control process. In this novel proposed framework, we combine advanced vibration-monitoring methods with the AI-driven prediction of the quality indicators to address this problem, increasing the quality, productivity, and efficiency of the process. The evaluation shows that the number of rejected parts, time devoted to reworking and manual finishing, and costs are reduced considerably. The framework adopts a generalized methodology to tackle the condition monitoring and quality control processes. This allows for a broader adaptation of the solutions in different CNC machines with unique setups and configurations, a challenge that other data-driven approaches in the literature have found difficult to overcome.<\/jats:p>","DOI":"10.3390\/s24010307","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T09:47:32Z","timestamp":1704361652000},"page":"307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Novel Framework for Quality Control in Vibration Monitoring of CNC Machining"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1664-0224","authenticated-orcid":false,"given":"Georgia","family":"Apostolou","sequence":"first","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]},{"given":"Myrsini","family":"Ntemi","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]},{"given":"Spyridon","family":"Paraschos","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5234-9795","authenticated-orcid":false,"given":"Ilias","family":"Gialampoukidis","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]},{"given":"Angelo","family":"Rizzi","sequence":"additional","affiliation":[{"name":"FIDIA S.P.A., 10099 Torino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-9178","authenticated-orcid":false,"given":"Stefanos","family":"Vrochidis","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-9020","authenticated-orcid":false,"given":"Ioannis","family":"Kompatsiaris","sequence":"additional","affiliation":[{"name":"Information Technologies Institute (\u0399\u03a4\u0399), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"ref_1","first-page":"1638","article-title":"Advantages and the future of CNC machines","volume":"2","author":"Mamadjanov","year":"2021","journal-title":"Sci. Prog."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1007\/s10845-020-01539-4","article-title":"The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)","volume":"31","author":"Jeon","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"859","DOI":"10.4028\/www.scientific.net\/AMM.657.859","article-title":"Smart Adaptive CNC Machining-State of the Art","volume":"657","author":"Vasiloni","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_4","first-page":"100009","article-title":"Machine learning and artificial intelligence in CNC machine tools, a review","volume":"2","author":"Soori","year":"2023","journal-title":"Sustain. Manuf. Serv. Econ."},{"key":"ref_5","first-page":"1392","article-title":"Development of Artificial Intelligence Algorithm for Automated CNC Machining Process for Unmanned Production","volume":"14","author":"Pande","year":"2023","journal-title":"J. Pharm. Negat. Results"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.cirpj.2022.06.001","article-title":"Infrastructure monitoring and quality diagnosis in CNC machining: A review","volume":"38","author":"Ntemi","year":"2022","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1007\/s00170-021-07107-1","article-title":"Creation of CNC system\u2019s components for monitoring machine tool health","volume":"117","author":"Martinova","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.procir.2013.09.045","article-title":"Monitoring systems for zero defect manufacturing","volume":"12","author":"Ferretti","year":"2013","journal-title":"Procedia CIRP"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.ijmachtools.2011.01.001","article-title":"Chatter in machining processes: A review","volume":"51","author":"Quintana","year":"2011","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106840","DOI":"10.1016\/j.ymssp.2020.106840","article-title":"Recent progress of chatter prediction, detection and suppression in milling","volume":"143","author":"Zhu","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ifacol.2019.10.022","article-title":"Control and monitoring for sustainable manufacturing in Industry 4.0: A literature review","volume":"52","author":"Santos","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, S., Li, W., Jianl, P., and Zhang, C. (2015, January 6\u20138). Development of a WSN based real time energy monitoring platform for industrial applications. Proceedings of the 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Calabria, Italy.","DOI":"10.1109\/CSCWD.2015.7230982"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11465-018-0499-5","article-title":"Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives","volume":"13","author":"Zheng","year":"2018","journal-title":"Front. Mech. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Angelopoulos, A., Michailidis, E.T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., and Zahariadis, T. (2019). Tackling faults in the industry 4.0 era\u2014A survey of machine-learning solutions and key aspects. Sensors, 20.","DOI":"10.3390\/s20010109"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s00170-019-03906-9","article-title":"Tool wear state recognition based on GWO-SVM with feature selection of genetic algorithm","volume":"104","author":"Liao","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alajmi, M.S., and Almeshal, A.M. (2021). Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization. Materials, 14.","DOI":"10.3390\/ma14143773"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1080\/08982112.2020.1813760","article-title":"HDP-HMM based approach for tool wear estimation and tool life prediction","volume":"33","author":"Han","year":"2021","journal-title":"Qual. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.ymssp.2019.06.021","article-title":"Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling","volume":"131","author":"Li","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3647","DOI":"10.1007\/s00170-019-04090-6","article-title":"Tool wear classification using time series imaging and deep learning","volume":"104","author":"Terrazas","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"203902","DOI":"10.1016\/j.wear.2021.203902","article-title":"Research on tool wear prediction based on temperature signals and deep learning","volume":"478","author":"He","year":"2021","journal-title":"Wear"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s00170-020-06354-y","article-title":"In-process tap tool wear monitoring and prediction using a novel model based on deep learning","volume":"112","author":"Xu","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"125010","DOI":"10.1088\/1361-6501\/ac22ee","article-title":"Tool wear estimation using a CNN-transformer model with semi-supervised learning","volume":"32","author":"Liu","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, R., Wang, J., Yan, R., and Mao, K. (2016, January 11\u201313). Machine health monitoring with LSTM networks. Proceedings of the 2016 10th International Conference on Sensing Technology (ICST), Nanjing, China.","DOI":"10.1109\/ICSensT.2016.7796266"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, J., Zhou, G., and Liao, X. (2018, January 25\u201327). Research on tool wear prediction based on LSTM and ARIMA. Proceedings of the 2018 International Conference on Big Data Engineering and Technology, Chengdu, China.","DOI":"10.1145\/3297730.3297732"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4391","DOI":"10.1007\/s00170-019-04916-3","article-title":"Modeling and analysis of tool wear prediction based on SVD and BiLSTM","volume":"106","author":"Wu","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.measurement.2020.108554","article-title":"Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning","volume":"173","author":"Ma","year":"2021","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42452-021-04427-5","article-title":"Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis","volume":"3","author":"Zhang","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"85903","DOI":"10.1109\/ACCESS.2021.3086667","article-title":"Research on fusion monitoring method of turning cutting tool wear based on particle filter algorithm","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108233","DOI":"10.1016\/j.ymssp.2021.108233","article-title":"In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling","volume":"164","author":"Zhang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s10462-012-9381-8","article-title":"Fuzzy logic for modeling machining process: A review","volume":"43","author":"Adnan","year":"2015","journal-title":"Artif. Intell. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.asoc.2018.03.043","article-title":"Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system","volume":"68","author":"Wu","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_34","first-page":"211","article-title":"Comparative study of ANN and ANFIS models for predicting temperature in machining","volume":"13","author":"Masoudi","year":"2018","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1016\/j.jclepro.2017.10.303","article-title":"Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy\u2013genetic algorithm technique toward sustainable machining","volume":"172","author":"Saw","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10845-020-01559-0","article-title":"Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining","volume":"32","author":"Xu","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6457","DOI":"10.1109\/TII.2020.3001054","article-title":"A data-driven approach of product quality prediction for complex production systems","volume":"17","author":"Ren","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1109\/TNNLS.2020.3001602","article-title":"A wide-deep-sequence model-based quality prediction method in industrial process analysis","volume":"31","author":"Ren","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_39","unstructured":"Su, Y., and Han, L. (2018). International Conference on Applications and Techniques in Cyber Security and Intelligence, Springer."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yeh, C.H., Fan, Y.C., and Peng, W.C. (2019, January 8\u201311). Interpretable multi-task learning for product quality prediction with attention mechanism. Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), Macao, China.","DOI":"10.1109\/ICDE.2019.00207"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Baumann, J., Wirtz, A., Siebrecht, T., and Biermann, D. (2020). Disturbance of the Regenerative Effect by Use of Milling Tools Modified with Asymmetric Dynamic Properties. J. Manuf. Mater. Process., 4.","DOI":"10.3390\/jmmp4030067"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.ijmecsci.2015.05.013","article-title":"Chatter identification methods on the basis of time series measured during titanium superalloy milling","volume":"99","author":"Jerz","year":"2015","journal-title":"Int. J. Mech. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.1007\/s00170-020-05303-z","article-title":"Technical data-driven tool condition monitoring challenges for CNC milling: A review","volume":"107","author":"Wong","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1007\/s00170-021-07325-7","article-title":"A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges","volume":"115","author":"Nasir","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ding, L., Sun, Y., and Xiong, Z. (April, January 31). Early chatter detection based on logistic regression with time and frequency domain features. Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Dubai, United Arab Emirates.","DOI":"10.1109\/AIM.2017.8014158"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s00170-016-9863-y","article-title":"ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system","volume":"89","author":"Hongya","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sener, B., Serin, G., Gudelek, M.U., Ozbayoglu, A.M., and Unver, H.O. (2020, January 10\u201313). Intelligent Chatter Detection in Milling using Vibration Data Features and Deep Multi-Layer Perceptron. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Virtual.","DOI":"10.1109\/BigData50022.2020.9378223"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.cirpj.2019.11.003","article-title":"On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition","volume":"28","author":"Otto","year":"2020","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.measurement.2018.06.006","article-title":"An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals","volume":"127","author":"Bu","year":"2018","journal-title":"Measurement"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1007\/s00170-020-06338-y","article-title":"Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach","volume":"111","author":"Proteau","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4387","DOI":"10.1007\/s00170-017-0476-x","article-title":"Early chatter detection in end milling based on multi-feature fusion and 3-sigma criterion","volume":"92","author":"Cao","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.precisioneng.2018.12.004","article-title":"Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling","volume":"56","author":"Bu","year":"2019","journal-title":"Precis. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1007\/s00170-021-07844-3","article-title":"Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique","volume":"117","author":"Li","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1007\/s00170-021-07027-0","article-title":"Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy","volume":"114","author":"Liu","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ymssp.2018.05.052","article-title":"Early chatter identification based on an optimized variational mode decomposition","volume":"115","author":"Sang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.11.046","article-title":"Chatter detection in milling process based on VMD and energy entropy","volume":"105","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.measurement.2021.109962","article-title":"Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis","volume":"184","author":"Tran","year":"2021","journal-title":"Measurement"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.matpr.2020.11.1005","article-title":"Exploration of tool chatter in CNC turning using a new ensemble approach","volume":"43","author":"Gupta","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_59","first-page":"4439","article-title":"Chatter prediction using merged wavelet denoising and ANFIS","volume":"23","author":"Kumar","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.jmapro.2019.05.046","article-title":"Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining","volume":"44","author":"Plaza","year":"2019","journal-title":"J. Manuf. Process."},{"key":"ref_61","unstructured":"FIDIA S.p.A (2023, October 03). Available online: https:\/\/www.fidia.it\/en\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/307\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:40:05Z","timestamp":1760103605000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010307"],"URL":"https:\/\/doi.org\/10.3390\/s24010307","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,4]]}}}