摘要
针对现场摩擦叠焊修复加工中高速液压驱动主轴头在低速时钻孔效率低的问题,提出了一种全液压驱动主轴水下钻孔特性建模与离线优化方法。通过多次实验获得钻孔数据,通过BP神经网络对数据进行拟合得到水下钻孔扭矩特性,并通过遗传算法寻优,得到了最优切削参数,该参数作为在线优化的初始输入。结果表明,通过该方法能够得到全液压驱动主轴的水下钻孔特性和最优切削参数,缩短了现场钻孔加工的在线优化时间,提高了钻孔加工效率。
Aiming at the low efficiencies in low speed drilling using high speed hydraulic drive spindles for in-situ friction stitch welding processes,a characteristic modeling and off-line optimization method of the underwater hydraulic drilling spindles in-situ machining systems were put forward.The drilling parameters and data were obtained by several tests.The optimal cutting parameters were obtained by the fitting based on BP neural network and genetic algorithm based optimization.The optimal parameters were used as the initial inputs for on-line optimization.The results show that the underwater drilling and the optimal cutting parameters may be effectively used in in-situ drilling processes,which shorten the on-line optimization time of drilling processes and improve the drilling efficiencies.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2018年第4期471-476,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(51775370)
关键词
现场加工
液压驱动
水下钻孔
BP神经网络
遗传算法
in-situ machining system
hydraulic drive
underwater drilling
BP neural network
genetic algorithm
