基于自适应控制技术的铣削参数优化

摘要

采用BP神经网络算法应用于铣齿功率建模能较准确地预测铣齿功率大小,进而运用STATISTICA的正交设计优化试验数据对滚齿机进行再制造,通过在主轴箱加设传感器实现了机床振动稳定性的的在线监控,分析各个切削状态下主轴箱振动同铣削功率的关系,进行优化切削参数,实现了数控系统与在线监控技术的自适应闭环监控.完成了4m大型滚齿机向高速铣齿机床SKX-4000的智能化再制造.结果表明,采用的控制策略能适应强力铣削的工况变化,稳定地控制加工过程,达到保护机床、刀具和提高加工效率的目的.%Using BP neural network theory and algorithms used in milling power modeling studies can more accurately predict the size of milling power, Through orthogonal design optimization of STAT1STI-CA test data for remanufacturing of gear hobbing, adding sensors in the spindle box, the numerical control system and adaptive control technology online monitoring is provided the vibration, Through the analysis of each state of the spindle box vibration cutting with milling power relations, then optimize the cutting parameters,the stability of the adaptive closed-loop is realized. Completed the 4 m large gear hobbing high speed milling gear to the intelligent machine remanufacturing. The results show that the control strategy can adapt to the operation condition of the powerful milling changes, Stability control of the process, protect the machine, tool,and the purpose of improving processing efficiency.