Vibration Signal-Based Tool Condition Monitoring Using Regularized Sensor Data Modeling and Model Frequency Analysis

Tool condition monitoring (TCM) plays a vital role in maintaining product quality and improving productivity in advanced manufacturing. However, complex machining environments often limit the monitoring accuracy of conventional monitoring systems. In the present study, a new diagnostic framework is proposed for TCM during machining using a novel regularization-based sensor data modeling and model frequency analysis. For the first time, the physical information of the underlying machining process is incorporated into the modeling procedure for the design of the associated regularization parameter. This ensures that significant underlying physics can be taken into account during the modeling so as to enhance the TCM performance. This idea is referred to as tool condition monitoring-oriented regularization (TCMoR). After a model has been identified from TCMoR-based sensor data modeling, the frequency-domain properties of the model are extracted to reveal unique and physically meaningful features of the underlying machining process for the TCM purpose. The effectiveness of the proposed diagnostic framework is validated by extensive in situ experimental studies under both variable and controlled tool-workpiece engagement conditions, demonstrating its advantages over conventional TCM methods and its potential applications in industry.