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.
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