A method for mining cutting force coefficient based on milling simulation and real-time data
Cutting force is one of the important parameters for monitoring the machining process and quality, and cutting force coefficient directly affects the prediction accuracy of cutting force.This paper proposes a cutting force coefficient mining method based on milling simulation and real-time data to improve the identification accuracy of cutting force coefficients.First, the cutting process dataset is obtained by experimental and simulation analysis methods.Second, a cutting force coefficient mining method based on tool rotation period is built, and the simulation data is used as a constraint condition for data mining to analyze the distribution characteristics of cutting force coefficients.Our research results indicate the distribution of periodic correlated cutting force coefficients exhibits a normal distribution form, and its accurate value is estimated through the normal distribution.This method effectively mines the cutting force coefficient and achieves accurate conversion of cutting data.The average error of data conversion is only 3％, achieving the same effect as experimental calibration and effectively improving the efficiency of cutting force coefficient identification.
millingmilling cutting force coefficientdata miningdata conversion
国家科技期刊平台
NSTL
万方数据
