Exploring Data Reduction Techniques for Additive Manufacturing Analysis

Additive manufacturing is a rapidly growing area that has the potential to revolutionize society. In order to better understand and improve this process, scientists and engineers conduct detailed studies on the applicability of various materials and the process that additively constructs the object. One method of analyzing the additive process is to use cameras to take images of the object as it is built layer by layer. As the complexity of the process, image resolution, and image capture frequency increases, so too does the volume of data generated, which can lead to data storage/movement issues. In this paper, we present an exploratory study of applying various lossless and lossy reduction techniques to an additive manufacturing data set from Los Alamos National Laboratory. Results show that SZ gives the best reduction ratio, ZFP yields the best accuracy, and Hybrid Data Sampling is the fastest method.