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

Published

November 13, 2024

Author(s)

Gi Suk Hong, Zhuo Yang, Yan Lu, Brandon Lane, Ho Yeung, Jaehyuk Kim

Abstract

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.

Proceedings Title

ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference

Conference Dates

August 25-28, 2024

Conference Location

Washington DC, DC, US

Conference Title

ASME IDETC-CIE 2024

Pub Type

Conferences

Download Paper

https://doi.org/10.1115/DETC2024-143601

Local Download

Keywords

additive manufacturing, model predictive control, multiscale process modeling, powder bed fusion

Citation

Hong, G. , Yang, Z. , Lu, Y. , Lane, B. , Yeung, H. and Kim, J. (2024), Multi-Scale Model Predictive Control for Laser Powder Bed Fusion Additive Manufacturing, ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington DC, DC, US, [online], https://doi.org/10.1115/DETC2024-143601, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957905 (Accessed March 31, 2025)