摘要
针对薄壁件铣削过程中刀齿半径不一致现象引起的铣削力系数计算失真问题,提出构造刀齿半径不一致时的实际铣削力系数,并采用核偏最小二乘法对不同铣削用量时的实际铣削力系数进行预测。针对两齿螺旋铣刀铣削过程推导理论铣削力系数,根据刀齿半径不一致铣削过程引入名义铣削力,推导刀齿半径误差,构造实际铣削力系数;基于核分析方法突出的非线性分析及预测能力,提出采用核偏最小二乘法在高维空间建立实际铣削力系数关于铣削用量及其组合量的预测模型,分析该方法中核主元个数、高斯核函数核参数对预测模型精度的影响并确定其取值范围。最后分析考虑刀齿半径误差与不考虑时的铣削力系数,并比较核偏最小二乘预测方法与偏最小二乘预测方法,结果表明所提铣削力系数构造过程及预测方法具有较高的计算精度和预测能力。
Concentrated on the distortion problem of milling force coefficient calculation caused by the different cutter tooth radii in thin-walled parts milling,actual milling force coefficient is constructed,and is predicted through kernel partial least square regression method when milling parameters change. To construct actual milling force coefficient,theoretical milling force coefficient is deduced for two-teeth spiral milling cutter,nominal milling force is defined based on the milling process with cutter of different tooth radii,and cutter tooth radius error is deduced. Prediction model of actual milling force coefficient about milling parameters and their compositional variables,is established in high dimension space through kernel partial least square regression method based on the prominent nonlinear analysis and prediction ability of nuclear analysis; kernel parameters of Gaussian kernel function and the number of kernel principal components,whose impact on prediction model is analyzed,are set in certain value range in kernel partial least square regression method. At last,milling force coefficients are analyzed respectively when cutter tooth radius error is considered or not,and kernel partial least square regression prediction method and partial least square regression prediction method are compared; according to above data,the proposed construction and prediction methods of milling force coefficient have high calculation accuracy and prediction ability.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2015年第19期197-205,共9页
Journal of Mechanical Engineering
基金
国家科技重大专项(2011ZX04016-021)
国家科技重大专项(2012ZX04005031)资助项目
国家科技支撑计划(2012BAF 01B02)
关键词
不一致刀齿
理论铣削力系数
名义铣削力
实际铣削力系数
核偏最小二乘
asymmetrical teeth
theoretical milling force coefficient
nominal milling force
actual milling force coefficient
kernel partial least square regression