摘要
对进给量、切削速度和轴向切深这3个切削参数对工件表面粗糙度和刀具振动幅度的影响进行试验研究。采用BBD响应面法对6061铝工件进行端铣加工试验,并通过数学建模对试验结果进行分析。提出一种基于遗传算法的多目标优化方法来同时减小工件表面粗糙度和刀具振动幅度。建立能预报表面粗糙度和刀具振动的径向基神经网络模型,并通过试验验证其准确性。
The effects of three cutting parameters,such as feed rate,cutting speed and axial depth of cut,on the surface roughness of the workpiece and the vibration amplitude of the tool were studied experimentally.The end milling test for 6061 aluminum work-piecewas carried out by BBD response surface method,and the experimental results were analyzed by mathematical modeling.A multi-ob-jective optimization method based on genetic algorithm was proposed to reduce the surface roughness and tool vibration amplitude.A ra-dial basis neural network model for predicting surface roughness and tool vibration was established and its accuracy was verified by experi-ments.
作者
李春雷
倪俊芳
LI Chunlei;NI Junfang(Department of Precision Manufacturing Engineering,Suzhou Vocational Institute of Industrial Technology,Suzhou Jiangsu 215104,China;School of Mechatronics,Soochow University,Suzhou Jiangsu 215021,China)
出处
《机床与液压》
北大核心
2019年第20期51-54,共4页
Machine Tool & Hydraulics
基金
江苏高校品牌专业建设工程资助项目(PPZY2015B186)
国家自然科学基金资助项目(51105263)
关键词
切削用量
表面粗糙度
刀具振幅
BBD响应面法
遗传算法
径向基神经网络
Cutting dosage
Surface roughness
Tool vibration amplitude
BBD response surface method
Genetic algorithm
Radial ba-sis neural network
