Characteristic Modeling and Optimization Method of Underwater Hydraulic Spindle Drilling Processes for In-situ Machining Systems

Abstract

Abstract: 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.

Key words: in-situ machining system, hydraulic drive, underwater drilling, BP neural network, genetic algorithm

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。

关键词: 现场加工, 液压驱动, 水下钻孔, BP神经网络, 遗传算法

CLC Number:

TH113.1

CHEN Yongliang1;PENG Tao1;LIU Deshuai 1;WANG Zhijiang2. Characteristic Modeling and Optimization Method of Underwater Hydraulic Spindle Drilling Processes for In-situ Machining Systems[J]. China Mechanical Engineering.

陈永亮1;彭涛1;刘德帅1;王志江2. 面向现场加工的全液压驱动主轴水下钻孔特性建模与优化方法[J]. 中国机械工程.

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