In-situ monitoring of image texturing via random forests and clustering with applications to additive manufacturing

Abstract

The amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.

Keywords:

• Statistical quality monitoring
• in-situ monitoring
• image
• random forests
• clustering
• additive manufacturing

Data availability

The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.24042891.v1.

Additional information

Funding

The present research was partially funded by ACCORDO Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano.

Notes on contributors

Fabio Caltanissetta

Fabio Caltanissetta received his doctoral degree in industrial engineering from Politecnico di Milano (while completing this research work), after completing an MSc in industrial engineering at the same university. He is currently a Process R&D Specialist at Caracol AM.

Luisa Bertoli

Laura Bertoli completed a Master of Science in industrial engineering at Politecnico di Milano, Italy (while completing this research work). She is currently a business data product specialist at UniCredit.

Bianca Maria Colosimo

Bianca Maria Colosimo is a professor in the Department of Mechanical Engineering of Politecnico di Milano. Her research interest is mainly in the area of big data mining for Industry 4.0, with special focus on advanced manufacturing. She is currently a department editor of IISE Transactions, senior editor of Informs Journal of Data Science, associate editor of Progress in Additive Manufacturing and Additive Manufacturing Letters. She has been editor-in-chief of the Journal of Quality Technology (2018-2021). She is included among the top 100 Italian woman scientists in STEM