Chatter in machining usually results in poor surface finish

reduces the life of machine tools and damage on cutters

and chatter suppression has always been the focus of academia and industry. The stability lobe diagram (SLD) based on the tool tip frequency response function (FRF) is the important basis for the existing chatter suppression methods. The impact test method is the most accurate method for obtaining the tool tip FRF

however

in application scenarios where the structure of the machine tool changes frequently

the efficiency of this method is not satisfactory. Therefore

a KNNbased tool tip FRF prediction method is proposed in this paper

which combines the impact test method with the KNN algorithm

there by greatly reducing the time consumption of the impact test method and accurately obtaining the tool tip FRF. In experimental verification

this method is compared with the RCSA method

and the results show the accuracy of the proposed method.