ENBIS: European Network for Business and Industrial Statistics

Spatio-Temporal PCA for Image Monitoring in Additive Manufacturing

Abstract

Submitted by

Bianca Maria Colosimo

Authors

Bianca Maria Colosimo (Politecnico di Milano), Marco Grasso (Politecnico di Milano)

Abstract

The contribution presents an approach to combine spatial and temporal information with Principal Component Analysis (PCA) and clustering to quickly detect out-of-control states from video images in metal 3D printing, also known as Additive Manufacturing. The main idea is to analyse video images using a PCA where a weighted variance-covariance matrix is considered in order to include information on spatial correlation of pixels in the image. An alarm rule based on clustering is further considered to detect hot-spots, i.e., location where the cooling transient is out of control with respect to the usual pattern. Comparison with other existing methods and application to a real case study are shown.

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