Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks
Journal:
arXiv
Published Date:
Jan 10, 2025
Abstract
Digital Twin -- a virtual replica of a physical system enabling real-time
monitoring, model updating, prediction, and decision-making -- combined with
recent advances in machine learning, offers new opportunities for proactive
control strategies in autonomous manufacturing. However, achieving real-time
decision-making with Digital Twins requires efficient optimization driven by
accurate predictions of highly nonlinear manufacturing systems. This paper
presents a simultaneous multi-step Model Predictive Control (MPC) framework for
real-time decision-making, using a multivariate deep neural network, named
Time-Series Dense Encoder (TiDE), as the surrogate model. Unlike conventional
MPC models which only provide one-step ahead prediction, TiDE is capable of
predicting future states within the prediction horizon in one shot
(multi-step), significantly accelerating the MPC. Using Directed Energy
Deposition (DED) additive manufacturing as a case study, we demonstrate the
effectiveness of the proposed MPC in achieving melt pool temperature tracking
to ensure part quality, while reducing porosity defects by regulating laser
power to maintain melt pool depth constraints. In this work, we first show that
TiDE is capable of accurately predicting melt pool temperature and depth.
Second, we demonstrate that the proposed MPC achieves precise temperature
tracking while satisfying melt pool depth constraints within a targeted
dilution range (10\%-30\%), reducing potential porosity defects. Compared to
PID controller, the MPC results in smoother and less fluctuating laser power
profiles with competitive or superior melt pool temperature control
performance. This demonstrates the MPC's proactive control capabilities,
leveraging time-series prediction and real-time optimization, positioning it as
a powerful tool for future Digital Twin applications and real-time process
optimization in manufacturing.
