TY - CONF A1 - Oster, Simon A1 - Breese, Philipp Peter A1 - Altenburg, Simon T1 - Porosity prediction in metal based additive manufacturing utilizing in situ thermography N2 - Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network. [1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022) https://dx.doi.org/10.1016/j.imatprotec.2021.117473 [2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013) https://dx.doi.org/10.1007/978-3-642-40669-0_33 T2 - KI-Tag Arbeitskreis Chemometrik & Qualitätssicherung - Chemometrics meets Artificial Intelligence CY - Berlin, Germany DA - 01.04.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - Defect Prediction KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-54621 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -