Laser powder bed fusion parameter estimation with k-NN

• Published in: International journal of advanced manufacturing technology Vol. 138; no. 3; pp. 1075 - 1092
• Main Authors: Jung, Patrick, DeVol, Nathaniel, Saldaña, Christopher, Fu, Katherine
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2025; Springer Nature B.V
• Subjects: Advanced manufacturing technologies; Algorithms; CAE) and Design; Computer-Aided Engineering (CAD; Engineering; Feature extraction; Industrial and Production Engineering; Lasers; Manufacturing; Mechanical Engineering; Media Management; Melt pools; Melting; Metal powders; Original Article; Parameter estimation; Powder beds; Production methods; Laser powder bed fusion; Additive manufacturing; Melt pool monitoring; Machine learning; Smart manufacturing
• ISSN: 0268-3768; 1433-3015; 1433-3015
• DOI: 10.1007/s00170-025-15591-y

Laser powder bed fusion (L-PBF) is a technique within additive manufacturing that 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, and 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. This work addresses the challenge by leveraging a k-nearest neighbor (k-NN) model to identify key features within melt pool imagery and predict the energy density. The k-NN model was trained on data provided by the National Institute of Standards and Technology (NIST). Data preprocessing was performed on the images to extract features that were used in the k-NN model. 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 energy density of different parts. A fivefold cross validation found the algorithm to be consistently predicting the class of 91.4% of the in situ melt pool images.