Data Analysis for Defect Monitoring in Additive Manufacturing – Applying Machine Learning to Predict Porosity in L-PBF
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2023
Författare
Sievers, Erik
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Laser powder bed fusion (L-PBF) is an additive manufacturing technique that sees
more and more use in industrial settings, but is held back by a lack of cost-effective
quality validation of created products. One core attribute of high-quality additive
manufactured products is a low porosity, i.e. a high ratio of solid to empty volume
inside the object. This thesis provides an overview of the state of the art for in-situ
monitoring of L-PBF manufacturing and investigates the use of outlier detection
methods as a way of encoding optical tomography data from an L-PBF process.
This is done using a commercial L-PBF machine with its accompanying in-situ
monitoring camera. The results show that outlier detection methods can be used
to detect porosity in created objects (0.94 - 0.99 ROC-AUC, receiver operating
characteristics’ area under curve) and that it can generalize between similar object
geometries. The thesis also provides a discussion of the limitations of the current
research and suggests future work both building upon the methods introduced in
the thesis and in the field of in-situ monitoring of L-PBF.
Beskrivning
Ämne/nyckelord
Machine Learning , Outlier Detection , Additive Manufacturing , Powder Bed Fusion , Optical Tomography , Porosity