( 1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 210009, China )
Abstract：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.
2020, 
