In-situ Quality Monitoring of Extrusion-based Additive Manufacturing via Random Forests and clustering

Abstract

The attention towards in-situ sensing in Additive Manufacturing has dramatically increased over the last years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images and videos. Insitu quality monitoring represents an opportunity for waste reduction and costs savings via inline detection of process flaws, which allows early identification of scraps and the possibility to correct process parameters for a first-time-right production. Despite of this great potential, no clear and assessed methodologies exist to automatically detect out-of-control states and defects occurrence via in-situ image analysis. This paper discusses opportunities and challenges of in-situ monitoring of Extrusion-based Additive Manufacturing processes by presenting a methodology for in-line defect detection based on stochastic textured surface modelling via Random Forests and k-means clustering for control charting. Significant advantages are shown thus presenting an interesting direction for future research.