一种数控机床自适应加工参数优化方法及系统
By establishing an adaptive machining parameter optimization system for CNC machine tools, and combining knowledge graphs and improved particle swarm optimization algorithms, machining parameters are adjusted in real time, solving the problem of low efficiency in parameter optimization in traditional methods. This achieves a dynamic balance between machining quality, efficiency, and cost, and improves the intelligence level of CNC machine tools.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANTONG GUBANG NUMERICAL CONTROL MASCH TOOL CO LTD
- Filing Date
- 2026-04-17
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional CNC machine tool machining parameter optimization methods rely on human experience, making it difficult to fully consider the coupled effects of multiple factors. They are prone to getting trapped in local optima, resulting in low optimization efficiency and difficulty in achieving a balance between machining quality, efficiency, and cost.
By collecting CNC machine tool parameters, material properties, tool parameters, and historical data, an initial database of machining parameters is established. A knowledge graph and estimation model of the machining process are constructed. An improved particle swarm optimization algorithm is used, combined with real-time working condition data, to perform adaptive parameter optimization and dynamically adjust the machining parameters to meet multiple constraints.
It enables rapid solution of the global optimal parameter combination under multiple constraints, reduces resource waste, improves machining stability, extends tool life, reduces production costs, has strong adaptive capabilities, and is suitable for various CNC machine tool machining scenarios such as metal cutting, milling, and drilling.
Smart Images
Figure CN122044070B_ABST