A 3D Deep Learning Model for Rapid Prediction of Structural Dynamics of Workpieces During Machining

Abstract Chatter stability in machining flexible parts depends directly on the structural dynamics of the workpiece. This paper proposes a novel two-stage framework that combines finite element (FE) and data-driven deep learning techniques to rapidly predict the varying dynamics of workpieces during machining. An automated framework is developed to create a large training dataset of CAD models with gradually-changing geometries. A deep 3D Convolutional Neural Network (3D-CNN) is developed to “learn” the variations in dynamic parameters as a function of geometry. The current model has been successfully implemented for prediction of natural frequencies of workpieces during turning operations. The proposed framework can be used as a computationally efficient tool in online process monitoring and automated correction applications.