{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T15:48:12Z","timestamp":1774280892948,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"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>Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model\u2019s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical\u2013virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool\u2019s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.<\/jats:p>","DOI":"10.3390\/s23125431","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T02:58:32Z","timestamp":1686193112000},"page":"5431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Digital Twin-Driven Tool Condition Monitoring for the Milling Process"],"prefix":"10.3390","volume":"23","author":[{"given":"Sriraamshanjiev","family":"Natarajan","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4866-1428","authenticated-orcid":false,"given":"Mohanraj","family":"Thangamuthu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3877-3063","authenticated-orcid":false,"given":"Sakthivel","family":"Gnanasekaran","sequence":"additional","affiliation":[{"name":"Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology (VIT), Chennai 600127, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4144-827X","authenticated-orcid":false,"given":"Jegadeeshwaran","family":"Rakkiyannan","sequence":"additional","affiliation":[{"name":"Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology (VIT), Chennai 600127, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.promfg.2019.06.096","article-title":"Application of Deep Visualization in CNN-Based Tool Condition Monitoring for End Milling","volume":"34","author":"Kothuru","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106946","DOI":"10.1016\/j.triboint.2021.106946","article-title":"Early wear detection and its significance for condition monitoring","volume":"159","author":"Lu","year":"2021","journal-title":"Tribol. 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