Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
For robotic WAAM practitioners, this work provides a practical data-driven framework to improve build geometry consistency under changing thermal conditions.
This paper tackles bead geometry control in Wire Arc Additive Manufacturing (WAAM) by learning a data-driven model using a recurrent neural network and one-step-ahead predictive control. The method achieves significant improvements in height and width consistency compared to constant input and static model baselines.
Robotics Wire Arc Additive Manufacturing (WAAM) is governed by complex and nonlinear process dynamics coupling thermal field to the build geometry. The process may be regarded as a multi-input/multi-output dynamical system with welding torch speed and wire feed rate as inputs and weld bead deposition height and width as outputs. In this paper, we use the input/output data to learn a data-driven model and use it for weld planning and control. We show that a simple recurrent neural network architecture and one-step-ahead predictive control can improve the process performance in terms of height and width consistency. To account for the changing thermal conditions during the printing process, we update the learning model using prediction error from the previous layer. This adaptation step further improves the prediction accuracy and controller performance. Experiments on a robotic WAAM testbed with integrated line-scanner feedback significant improvements in height and width consistency compared to constant input and static model baselines. The proposed learning and adaptation framework provides a practical pathway toward robust, data-driven regulation of additive manufacturing processes.