The patent innovatively applies industrial big data and intelligent algorithms to the field of precision machining. Through real-time collection of machine vibration, cutting force and other working condition data, intelligent optimization of cutting parameters, significantly improve machining efficiency and extend tool life. The technology adopts cloud collaboration architecture, supports remote monitoring and edge computing, and can be accumulated to form an enterprise-specific process database, which can help customers reduce processing costs by more than 15%, shorten the process debugging cycle by 30%, and significantly improve the level of intelligent manufacturing.

The patent innovatively integrates multi-source sensor data and machine learning algorithms to establish a high-precision tool wear prediction model through real-time acquisition of cutting force, vibration, acoustic emission and other key parameters. The technology breaks through the limitations of traditional manual detection to achieve intelligent assessment and early warning of the remaining life of the tool, with a prediction accuracy of more than 90%, which can help companies optimize tool change strategies, reduce tool costs by more than 15%, and reduce unplanned downtime by 30%. It provides a reliable tool health management solution for intelligent manufacturing.