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MATLAB-Based Predictive Monitoring of Tool Wear Using Vibration Signals and Modal FEA
Introduction :
Introduction :
In modern manufacturing environments, tool wear is a critical factor influencing product quality, dimensional accuracy, and production efficiency. Over time, cutting tools degrade due to mechanical, thermal, and frictional stresses, leading to increased vibrations, poor surface finish, and even catastrophic tool failure. Traditional wear monitoring techniques often involve manual inspection or off-line analysis, which are time-consuming and interrupt the machining process.
To address this, the development of real-time, intelligent monitoring systems is gaining attention. This project focuses on simulating a predictive tool wear monitoring system using MATLAB/Simulink architecture. By analyzing vibration signals sensed during the machining process, the system identifies wear progression and predicts tool health. Finite Element Analysis (FEA) is integrated with vibration signal processing to extract mode shape shifts, stiffness variations, and severity indices, enabling a more accurate diagnosis of tool wear. A predictive model then classifies the tool condition and alerts the user via a graphical interface.
Objectives :
Objectives :
To simulate a machining environment in MATLAB/Simulink that reflects tool wear progression under dynamic conditions.
To model a vibration-based sensor system that detects real-time tool behavior and collects relevant signal data.
To integrate a microcontroller module that emulates signal acquisition and preprocessing similar to a real-world embedded system.
To perform FEA-based vibration analysis for extracting features like stiffness, severity index, and mode shape shifts associated with tool degradation.
To develop a predictive model that classifies the tool's wear state using the extracted features and triggers real-time alerts.
To build a user interface (GUI) that displays tool condition status and logs the analysis results for further review and traceability.
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