# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 1 Introduction
Triaxial cutting forces and vibration signals are collected and then preprocessed by wavelet packet denoising. The time, frequency, and time-frequency domain features are extracted to reduce information redundancy, and the kernel principal component analysis is applied to fuse the most sensitive characteristics. A fusion model combining least squares support vector regression and particle swarm optimization algorithms is established to learn the dependency relationship between the fused features and tool flank wear. They conducted milling experiments under multiple working conditions to validate the effectiveness of the proposed method. The experimental results show that the overall performance of the proposed method is superior to that of other comparison methods. Cutting forces play a crucial role in the machining process, providing vital information about the cutting tool's condition, being widely applied in various aspects of manufacturing. Firstly, by monitoring the changes in cutting force, effective cutting-tool wear monitoring can be achieved. As the cutting tool wears down, the magnitude and trends in the cutting force undergo fluctuations, and feature extraction of the cutting force can be used for analyzing the cutting tool's condition. Secondly, vibration analysis of cutting forces can be utilized to assess the stability of a machine tool, thereby detecting the presence of vibrations or resonances. Additionally, abnormal variations in cutting forces may indicate collisions between the tool and the workpiece or fixture, facilitating collision detection to ensure production stability and prevent damage. Cutting force data can also be employed for optimizing cutting parameters by establishing cutting force models to enhance processing efficiency and quality. Machine learning and ensemble learning techniques have demonstrated excellent performance in constructing cutting-tool wear prediction models. Machine learning can automatically learn and extract complex features, enhancing the model's generalization capability. Ensemble learning, combining predictions from multiple models, effectively overcomes the limitations of individual models, thereby improving the overall predictive performance. Through these technologies, it can more accurately predict the trends in cutting-tool wear variations, achieving precise control over the cutting processes. Cutting force is a complex nonlinear function influenced by various factors, such as material properties, cutting-tool geometry, and cutting conditions. Utilizing cutting force as input parameters for prediction models comprehensively reflects the characteristics and variations of the cutting processes. By establishing cutting-tool wear models, the production line can promptly initiate cutting-tool replacement, thereby reducing downtime and production costs. This study aims to identify the better assisted milling technology and optimal process parameter combinations for SKD11 hardened steel to enhance cutting performance and machining quality. Based on these foundations, a cutting-tool wear prediction model through machine learning is further developed to improve the production efficiency and cost reductions. The model can predict cutting-tool wear in real-time, reducing the replacement times of premature tool disposal, enhancing the rate of high-quality products, minimizing the degradation of cutting quality due to excessive tool wear, and further enabling intelligent automatic cutting-tool changing. The goal is to achieve automated and intelligent manufacturing that maintains both product quality and high efficiency in the production of hardened steels.