Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning

In manufacturing automation, accurately determining the optimal tool replacement timing is critical yet challenging. Tool condition monitoring (TCM) has been widely studied to address this issue. Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. Then, vibration data collected from the spindle are used to train an LSTM model, which is effective for time-series data processing. The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. Experimental results show that the developed model accurately estimates cutting forces using short segments of vibration data from a single tool revolution. Additionally, the observed relationship between cutting force and tool wear remains consistent across different machining conditions. This study validates real-time cutting force estimation via spindle vibration monitoring and suggests its potential for tool wear prediction. The proposed method offers a practical, low-cost solution for improving tool condition monitoring in automated machining.