Harnessing On-Machine Metrology Data for Prints with a Surrogate Model for Laser Powder Directed Energy Deposition

TL;DR

This paper develops a physics-informed surrogate model using on-machine metrology data and Dynamic Mode Decomposition with Control to predict key outcomes in Laser Powder Directed Energy Deposition, enabling real-time process monitoring and control.

Contribution

It introduces a novel data-driven surrogate modeling approach that captures complex physics of LP-DED using extensive metrology data and DMDc, including uncertainty quantification.

Findings

High accuracy predictions of melt pool temperature and size.

Model operates in real-time at process-relevant speeds.

Validated on unseen parts with strong agreement to measurements.

Abstract

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. This methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.