Mechanical & Materials Engineering, Department of
Prediction of Meltpool Depth in Laser Powder Bed Fusion Using In-Process Sensor Data, Part-Level Thermal Simulations, and Machine Learning
Kevin D. Cole
Date of this Version
The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the aforementioned objective, twenty-one Inconel 718 samples were designed and built with a variety of processing parameters ranging from a power of 200 W to 370 W and a scan speed of 670 mm/s to 1250 mm/s. These parts were characterized and the meltpool depth was measured through optical microscopy. A combination of part-level thermal simulations from a spectral graph theory method and in-process sensor data from infrared thermal camera and a meltpool imaging pyrometer were used as inputs to simple machine learning models to predict the meltpool depth. The meltpool depth was correctly predicted with an accuracy of F-Score 85.9%. This exploratory work provided an avenue for rapid prediction of microstructure evolution in metal additive manufacturing.
Advisors: Jeffrey Shield and Prahalada Rao
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Mechanical Engineering & Applied Mechanics, Under the Supervision of Professor Jeffrey Shield & Professor Prahalada Rao. Lincoln, Nebraska: December 2022
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