Rеsеаrсh оn thе Milling Stаbilitу Prеdiсtiоn undеr vаrуing Tооl-hоldеr Аssеmblу Ваsеd оn Trаnsfеr Lеаrning

Repeated impact tests will be conducted to obtain the tool tip frequency response functions (FRFs) for analyzing the milling stability with variable tool-holder clamping conditions. Considering the low efficiency, a transfer learning-based method is proposed to predict the milling stability of the target tool using the tool tip FRFs measured under a few overhang lengths. The tool tip FRFs under multiple overhang lengths of the source tool and a few overhang lengths of the target tool are measured, which are taken to perform the milling stability analysis and construct the source and target domain data. A similarity matching is conducted on the source and target domains to select appropriate source domain samples. Then, the neural network and TrAdaBoost transfer learning algorithm are combined to adaptively update the weights of target and source samples, and a classifier for obtaining milling vibration states is trained iteratively. Three tool-holder combinations are taken to perform the case study. The accuracies of two classifiers for two target tool-holder combinations are improved by 10.93% and 6.25% respectively after introducing the transfer learning, and the feasibility of the proposed transfer learning-based milling stability prediction method is validated by the milling experiments. © 2024 Chinese Mechanical Engineering Society. All rights reserved.