Publisher : The Korean Society of Mechanical Engineers
DOI : 10.22634/KSME-A.19184.108.40.2069
Title & Authors
Specific Cutting Force Coefficients Modeling of End Milling by Using Neural Network Lee, Sin-Young; Lee, Jang-Moo;
In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on, and cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling process for various cutting conditions, a mathematical model is important and this model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging farces of cutting tests. In this paper, the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in teaming stage as the omitted numbers of experimental data increases the average errors increase.
Specific Cutting Force Coefficients;End Milling;Cutting Dynamics;Chip Load;Neural Network;Cutting Experiments;