Image recognition based on machine learning and wireless sensor networks for predicting material loss in milling cutters

With the continuous improvement of machining accuracy and efficiency requirements in industrial manufacturing, the loss prediction of milling cutter materials has become an important part of optimizing the production process. Traditional loss forecasting methods often rely on manual monitoring and experience, which is low in efficiency and difficult to adapt to complex production environment. This study aims to combine machine learning technology and wireless sensor network to build an efficient milling cutter material loss prediction model, so as to realize real-time monitoring and accurate prediction, and improve production efficiency and material utilization. A wireless sensor network-based monitoring system was deployed to collect data such as vibration, temperature, and acoustic signals during milling in real time. Through a comprehensive process of data preprocessing and meticulous feature extraction, various machine learning algorithms, including but not limited to random forests and support vector machines, are employed to analyze the data that has been meticulously collected. Once the modeling is complete, the predictive performance of these algorithms is critically evaluated by comparing their outputs to actual loss data recorded during operations. The experimental findings indicate that the monitoring system, which is underpinned by a wireless sensor network, is capable of delivering real-time insights into the operational status of the milling cutter. Furthermore, the machine learning model demonstrates impressive accuracy in predicting material loss. This accuracy marks a significant advancement over traditional methods, enhancing both the precision of predictions and the speed of responses. As a result, the framework facilitates effective forecasting of milling cutter material loss, thereby offering an innovative solution that aligns with the principles of intelligent manufacturing.