Towards Sustainable Precision Manufacturing: Sensor-Driven Vibration Suppression and Adaptive Control in CNC Machining

In the field of high-precision CNC machining, the vibration problem of the electromechanical system has always been an important challenge that restricts the improvement of machining quality. It is one of the key factors for the long-term stable operation of equipment. Traditional vibration control methods focus on structural vibration isolation and passive damping. This feature is difficult to meet the higher requirements of modern intelligent manufacturing for sub-micron precision and dynamic response. Therefore, based on the concept of multi-sensor fusion, this study proposes a vibration suppression and optimization control scheme that integrates high-precision vibration signal acquisition, feature intelligent recognition framework, and breakthrough design real-time feedback control. At the same time, using the joint extraction algorithm of time domain and frequency domain features, this study classifies and models the perturbation sources generated during the processing process. The study further introduced an optimization control strategy with self-learning capability. This strategy enables the system to perform online parameter adjustment and closed-loop error compensation in a high-speed dynamic environment. This can effectively suppress resonance and interference caused by spindle inertia, tool run-out, and work-piece stiffness changes. The experimental results show that this method can reduce the overall vibration amplitude of the system, while improving the positioning accuracy and significantly reducing energy consumption.