Session: CIE-18-02 SEIKM: Systems Engineering and Complex Systems

Paper Number: 143601

143601 - Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing 

Additive manufacturing (AM) process stability is critical for ensuring part quality. Model Predictive Control (MPC) has been widely recognized as a robust technology for controlling manufacturing processes across various industries. Despite its widespread use, there has been limited exploration into the application of real-time MPC for controlling the laser powder bed fusion (LPBF) AM process through in-situ process monitoring. This paper introduces a novel framework for developing MPC strategies for real-time LPBF control, accommodating various multi-scale approaches—pointwise, trackwise, layerwise, and partwise. This framework considers the diverse needs for material state representation when formulating predictive models, constraints, and objective functions while allowing for predictive control implementation at different scales and frequencies. The utility of this framework is demonstrated through three trackwise MPC case studies, all employing high-speed co-axial melt pool imaging. Simulation results indicate that LPBF systems enhanced with MPC achieve superior performance compared to those governed by open-loop control systems. Additionally, we find that MPC implementations that utilize feedback control at finer scales provide improved process stability, albeit at the expense of increased computational demands. This framework serves as a guide for industrial practitioners, outlining how the implementation of MPC in AM process control can be optimized based on available in-situ sensing capabilities and data acquisition techniques.

Presenting Author: Gi Suk Hong National Institute of Standards and Technology(NIST)

Presenting Author Biography: Gi Suk is a Ph.D. candidate student at Pohang University of Science and Technology(POSTECH). He has also been working at NIST, Information Modeling and Test Group, as a foreign associate researcher since January 2024. Gi Suk's research topic of interest is process control of AM with monitoring data. He is an expert in data analytics and implements ML/DL techniques for it.

Authors:

Gi Suk Hong National Institute of Standards and Technology(NIST)
Jaehyuk Kim National Institute of Standards and Technology(NIST)
Zhuo Yang National Institute of Standards and Technology(NIST)
Yan Lu National Institute of Standards and Technology(NIST)
Brandon Lane National Institute of Standards and Technology(NIST)
Ho Yeung National Institute of Standards and Technology(NIST)