Multimode tool tip dynamics prediction based on transfer learning

Abstract

Abstract Chatter suppression is a classic research topic for improving productivity in real manufacturing industry. One of the most challenging tasks in chatter suppression is to predict tool tip dynamics in the whole workspace, especially the multimode conditions. Traditional prediction methods based on finite element analysis or kinematic modeling are either time-consuming or inaccurate. To address this subject, this paper proposes a transfer learning based multimode tool tip dynamics prediction method. A tool with multimode dynamics at the tool tip is first selected as the source tool and sufficient impact tests are carried out within its workspace to construct the source tool tip dynamics dataset. For a new tool, namely the target tool, the workspace is first divided according to the modal orders of the FRFs in the source dataset. In each of the sub-workspaces, only few impact tests are required to establish a target dataset for training the tool tip dynamics prediction model assisted by the source dataset with a transfer learning algorithm. Finally, the tool tip dynamics prediction model of the target tool in the whole workspace can be constructed by integrating the prediction models in all sub-workspaces. To demonstrate the performance of the proposed method, a five-axis machine tool along with three different cutting tools are selected for providing a detailed experimental validation.