Cutting chatter can lead to problems such as poor surface quality of the machined workpiece

reduced material removal rate and increased tool wear. The tool tip modal parameters are essential inputs for constructing stability lobe diagrams and selecting chatter free machining parameters. However

in the machining process

the tool tip modal parameters change with the tool pose and the tool changes frequently

and the classical impact test method has low efficiency and high cost

so how to accurately and efficiently predict the tool tip modal parameters under the changed pose has become an urgent problem to be solved in the cutting process. In this paper

combined with the idea of transfer learning

a method of modal parameter prediction based on multi-source transfer learning is proposed. When a new tool is used

the tool point modal parameters under only a few positions need to be measured through impact test

and then the tool point modal parameter prediction model for the new tool can be obtained by multi-source transfer combined with the modal parameter data of multiple existing tools. Finally

a practical experiment on a five-axis machine tool shows that the proposed method is effective.