Laser Powder Bed Fusion (L-PBF) is a technique within additive manufacturing which uses a high power density laser to build parts from fused powdered metal alloy. This technology is well equipped to produce complex parts with otherwise impossible features such as hidden voids or lattice structures. Alongside capability, reliability and quality are key characteristics considered when choosing a manufacturing method, and these are gaining attention as this method becomes more prevalent in industry. One main indicator of a stable L-PBF process is consistent melt pool geometry, the properties of which are likely to determine the quality of the part produced. As computing power and sensing technologies become more advanced, this melt pool geometry could be studied in real time. Therefore, the loop could be closed on process monitoring in order to achieve optimal quality. This work addresses that challenge by capitalizing on machine learning techniques to reduce the latency between sending process commands and receiving process validation. This thesis presents a novel application of a k-nearest neighbor (k-NN) model to identify key parameters within melt pool imagery and predict significant process parameters. The k-NN model was trained on data provided by the National Institute of Standards and Technology (NIST). This approach was used to accurately infer the energy density of unseen layers within the same part. The algorithm was subsequently tested with unique scan strategies and found to reasonably estimate the process parameters of different parts. A 5-fold cross validation found the algorithm to be consistently predicting the class of 95.42% of the in-situ melt pool images.
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MATIN Development Team