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Digital Twin-Driven Tool Condition Monitoring for the Milling Process
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 over...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302249/ https://www.ncbi.nlm.nih.gov/pubmed/37420597 http://dx.doi.org/10.3390/s23125431 |
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author | Natarajan, Sriraamshanjiev Thangamuthu, Mohanraj Gnanasekaran, Sakthivel Rakkiyannan, Jegadeeshwaran |
author_facet | Natarajan, Sriraamshanjiev Thangamuthu, Mohanraj Gnanasekaran, Sakthivel Rakkiyannan, Jegadeeshwaran |
author_sort | Natarajan, Sriraamshanjiev |
collection | PubMed |
description | 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’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s 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. |
format | Online Article Text |
id | pubmed-10302249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103022492023-06-29 Digital Twin-Driven Tool Condition Monitoring for the Milling Process Natarajan, Sriraamshanjiev Thangamuthu, Mohanraj Gnanasekaran, Sakthivel Rakkiyannan, Jegadeeshwaran Sensors (Basel) Article 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’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s 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. MDPI 2023-06-08 /pmc/articles/PMC10302249/ /pubmed/37420597 http://dx.doi.org/10.3390/s23125431 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Natarajan, Sriraamshanjiev Thangamuthu, Mohanraj Gnanasekaran, Sakthivel Rakkiyannan, Jegadeeshwaran Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title | Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title_full | Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title_fullStr | Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title_full_unstemmed | Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title_short | Digital Twin-Driven Tool Condition Monitoring for the Milling Process |
title_sort | digital twin-driven tool condition monitoring for the milling process |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302249/ https://www.ncbi.nlm.nih.gov/pubmed/37420597 http://dx.doi.org/10.3390/s23125431 |
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