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Meeting Materials Science & Technology 2020
Symposium Additive Manufacturing: Qualification and Certification
Presentation Title Similarity Analysis and Clustering of Thermal History to Understand Process-structure Relationships
Author(s) Sujana Chandrasekar, Jamie B Coble, Amy Godfrey, Serena Beauchamp, Fred List III, Vincent Paquit, Sudarsanam Suresh Babu
On-Site Speaker (Planned) Sujana Chandrasekar
Abstract Scope Part qualification is challenging in AM processes like selective laser melting (SLM) due to variation in microstructure and associated properties with changing thermal history. Thermal cycling is inherent to SLM and dictated by laser parameters, scan patterns, geometry and defects. In this work, we develop time series analysis methods to identify similarities in thermal history using data from infrared monitoring of SLM process. Similarity is defined using Euclidean distance between infrared data from individual points across layers and time. Clustering is done using results of similarity analysis. Clustering results indicate regions with similar thermal histories within different layers of the part. Clusters change with scan angle and part cross-section. Clustering results are correlated with characterized microstructures, which change with thermal history. This work demonstrates potential in time series analysis of infrared data for understanding process-structure relationships in AM, which can be complemented by thermal modeling.
Proceedings Inclusion? Undecided


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