# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 5 Conclusions  
In this study, a machine learning approach was developed to accurately estimate cutting forces using spindle vibration signals. Unlike conventional force measurement methods, which require dynamometers or indirect sensor-based approaches, the proposed model enables real-time estimation of cutting forces solely from vibration data. Experimental investigations were conducted to analyze the relationship between tool wear and cutting force variations, and a machine learning model was constructed to estimate cutting forces with high accuracy. Additionally, the correlation between \(F\_x\) and \(F\_y\) was examined, allowing for further analysis of cutting force variations in relation to tool wear progression. To evaluate the performance of the proposed model, a comparative study was conducted against a fully dense neural network model. The results demonstrated that the LSTM-based model achieved significantly lower estimation errors and demonstrated superior accuracy in estimating the correlation coefficient of cutting force variations during machining. Furthermore, the proposed model successfully estimated cutting forces using spindle vibration data collected within a single tool revolution, confirming its feasibility for real-time tool condition monitoring. In conventional vibration-based tool condition monitoring (TCM) systems, advanced signal processing techniques are often required, involving complex filtering methods and anomaly detection algorithms to isolate meaningful signals. In contrast, this study employed a simple filtering method to effectively remove non-cutting signals while preserving the essential vibration data needed for force estimation. By eliminating the need for a high-cost dynamometer, the proposed method provides a practical and efficient alternative for real-time force estimation in machining environments. The findings of this study demonstrate the potential of spindle vibration-based cutting force estimation as a viable approach for monitoring machining processes. While the estimation of cutting force correlation coefficients may provide additional insights into tool wear trends, this remains a secondary aspect of the study. Future research will focus on evaluating the robustness of the proposed algorithm across diverse machining conditions and further refining the estimation accuracy under varying operational environments.  
Conceptualization, J.-D.R., H.-H.L., K.-N.H., S.-R.K. and M.C.L.; Methodology, J.-D.R., S.-R.K. and M.C.L.; Software, J.-D.R.; Translation, J.-D.R., H.-H.L. and K.-N.H.; Resources, H.-H.L. and K.-N.H.; Data curation, J.-D.R.; Writing--original draft, J.-D.R.; Writing--review & editing, J.-D.R. and M.C.L.; Supervision, H.-H.L., K.-N.H. and S.-R.K.; Funding acquisition, S.-R.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Institute of Industrial Technology (KITECH) under the \"Establishment of BIZ platform for advancement of Ppuri industry\" (KITECH MZ240010) and by the Ministry of Trade, Industry and Energy under the \"Development of Class 1E EHO Control Valve Localization\" (NS240048).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON], [PERSON], [PERSON] were employed by the company Korea Institute of Industrial Technology. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.