C5.4 - From Thermographic In-situ Monitoring to Porosity Detection - A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion
- Event
- SMSI 2023
2023-05-08 - 2023-05-11
Nürnberg - Band
- Lectures
- Chapter
- C5 - Modern developments in measurement and sensor technology with application focus
- Author(s)
- S. Oster, N. Scheuschner, K. Chand, P. Breese, T. Becker, S. Altenburg - Bundesanstalt für Materialforschung und -prüfung, Berlin (German), F. Heinrichsdorff - Siemens AG, Berlin (Germany)
- Pages
- 179 - 180
- DOI
- 10.5162/SMSI2023/C5.4
- ISBN
- 978-3-9819376-8-8
- Price
- free
Abstract
In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk.