A Learn-and-Control Strategy for Jet-Based Additive Manufacturing

In this article, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model's parameters are obtained by training the network through...

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Veröffentlicht in:	IEEE/ASME transactions on mechatronics 2022-08, Vol.27 (4), p.1946-1954
Hauptverfasser:	Inyang-Udoh, Uduak, Chen, Alvin, Mishra, Sandipan
Format:	Artikel
Sprache:	eng
Schlagworte:	3D printing Adaptation models Additive manufacturing Algorithms Back propagation networks Control systems Deep learning Feedback control Feedforward control Feedforward systems Geometry Lead time Machine learning Manufacturing Mathematical models Parameters Prediction algorithms Predictive control Predictive models Recurrent neural networks Tracking errors
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