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Attended Meeting for NSF ERC Student Leaders
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Attended NSF Biennial Meeting
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portfolio
Portfolio item number 1
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Portfolio item number 2
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publications
Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks
Published in Journal of Manufacturing Systems, 2025
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 Proportional–Integral–Derivative (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.
Recommended citation: Y.-P. Chen, V. Karkaria, Y.-K. Tsai, F. Rolark, D. Quispe, R. X. Gao, J. Cao, W. Chen, “Real-time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks,” Journal of Manufacturing Systems, vol. 80, pp. 412–424, Jun. 2025, doi: 10.1016/j.jmsy.2025.03.009.
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talks
Development of a Powder Delivery Control Model for Precise Multimaterial Deposition via Laser Directed Energy Deposition
Published:
Powder-blown laser directed energy deposition (L-DED) expanded the material design space by offering the ability to deposit multiple powder feedstocks simultaneously, enabling the fabrication of functionally graded materials. However, achieving the desired material distribution in multimaterial parts presents challenges due to transient deposition behavior resulting in irregular gradients. Here, we developed a model for precise powder delivery control in a custom L-DED system by tuning multiple powder flow rates to achieve varying alloy gradients and correlating powder flow with feedstock proportions in the printed blend. Powder flow was modeled as a first-order transfer function with an initial delay for powder transport. The proportions of each constituent alloy in the printed blend were interpolated based on the concentration of elements unique to each feedstock, characterized via energy-dispersive X-ray spectroscopy. This work provides crucial insights for the design and optimization of complex multimaterial distributions in functionally graded materials for high-performance applications.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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