Additive Manufacturing Data Analytics, Process to Performance Analysis

Additive manufacturing (AM) is a digital process that provides critical information across the entire process lifecycle from product definition, manufacturing, test/inspection and final acceptance. This information is comprised of large spatial and temporal data sets that provide insights to the quality of the final manufactured part. These data sets currently reside on disparate systems which can limit correlation and advancement of AM process understanding and insights. There is a desire to integrate these data sets into a "super set" of data that is registered and fused together using data consolidation and visualization techniques. With the data integrated, there is the opportunity to apply new data analysis techniques to infer relationships between in process, post process and performance data. The student team worked to enhance data analytics applied to existing UW data sets through the development of new software packages that enable large AM data sets to be integrated.

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