Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Abstract

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.

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

“…Other examples include intelligent transportation networks with adaptive traffic management that can self-adapt to mitigate congestions [ 58 ]. Other examples in smart manufacturing facilities include systems with predictive maintenance strategies, forecasting potential equipment failures and optimizing operational efficiency [ 11 , 59 , 60 ]. Additionally, these systems excel in fault diagnosis by promptly identifying and diagnosing anomalies in real-time data streams [ 61 , 62 , 63 ].…”

Digital Twin Platform for Water Treatment Plants Using Microservices Architecture

“…One of the main goals while developing the Digital Twin platform for a water treatment plant is the implementation of a predictive maintenance service. This service is a crucial component that can significantly reduce downtime and maintenance costs [ 34 ]. The service developed in this framework uses machine learning techniques to predict potential failures in the plant’s equipment, specifically focusing on water pump drivers.…”

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

“…Qiao et al [151] proposed a datadriven DT model and a hybrid model prediction method based on deep learning, which demonstrated the accuracy of tool wear prediction through the study of vibration data from a milling machine. In addition, Natarajan et al [152] proposed a technique that utilizes DTs to construct a balanced virtual instrumentation framework that is perfectly matched to the physical system to achieve exceptional accuracy in inspecting and predicting tool conditions. The tool condition monitoring system deploys the DT model to predict different tool conditions based on sensory data.…”