Intelligent Control Console System based on Deep Learning: Key Path and Warning Mechanism Optimization for Improving CNC Machining Efficiency

Abstract

The increasing complexity of modern CNC (Computer Numerical Control) machining processes necessitates advanced systems to enhance operational efficiency and precision. This paper presents an intelligent control console system based on deep learning, designed to optimize key machining paths and warning mechanisms, ultimately improving CNC machining efficiency. The proposed system integrates reinforcement learning and deep neural networks to dynamically adjust machining parameters such as cutting speed, feed rate, and tool selection, ensuring optimal performance under varying operational conditions. By leveraging real-time sensor data, the system continuously monitors tool wear, machine health, and process performance, providing early fault detection and predictive maintenance capabilities. Moreover, it incorporates an optimized warning mechanism that alerts operators about potential failures, tool malfunctions, or deviations from expected performance, enabling timely interventions. The effectiveness of the system is demonstrated through its ability to reduce downtime, enhance tool life, and improve product quality by fine-tuning machining parameters based on continuous feedback. Real-time adaptive adjustments of parameters and multi-objective optimization for machining time, energy consumption, and tool wear further contribute to the system's operational efficiency. Additionally, the integration of IoT sensors with deep learning models enhances the system’s predictive capabilities, ensuring high levels of accuracy and decision-making support for CNC operators. This paper presents a novel approach to intelligent CNC machining control, providing significant contributions to the development of smart manufacturing systems.