Data Analysis for Defect Monitoring in Additive Manufacturing – Applying Machine Learning to Predict Porosity in L-PBF
| dc.contributor.author | Sievers, Erik | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Gulisano, Vincenzo | |
| dc.contributor.supervisor | Papatriantafilou, Marina | |
| dc.date.accessioned | 2023-03-28T08:17:44Z | |
| dc.date.available | 2023-03-28T08:17:44Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | 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. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/306019 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Machine Learning | |
| dc.subject | Outlier Detection | |
| dc.subject | Additive Manufacturing | |
| dc.subject | Powder Bed Fusion | |
| dc.subject | Optical Tomography | |
| dc.subject | Porosity | |
| dc.title | Data Analysis for Defect Monitoring in Additive Manufacturing – Applying Machine Learning to Predict Porosity in L-PBF | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Computer science – algorithms, languages and logic (MPALG), MSc |