TY - CONF A1 - Oster, Simon T1 - A comparison of machine learning approaches for porosity prediction in PBF-LB/M based on thermography N2 - Metal-based additive manufacturing processes are increasingly used in industry to produce complex-shaped components. In this regard, the laser-based Powder Bed Fusion process (PBF-LB/M) is one of the key technologies due to its capability to produce components in high spatial accuracy. The formation of porosity during manufacturing poses a serious risk to the safety of the printed parts. For quality assessment, in-situ monitoring technologies such as thermography can be used to capture the thermal history during production. It was shown that discontinuities within the thermal history can be correlated with the probability of porosity or defect formation. In this context, Machine Learning (ML) algorithms have achieved promising results for the task of porosity prediction based on thermographic in-situ monitoring data. One important technique is the use of thermogram features for porosity prediction that are extracted from the raw data (e.g., features related to the melt pool geometry or spatter generation). However, the reduction from large thermogram data to discrete features holds the risk of losing potentially important thermal information and, thereby, introducing bias in the model. Therefore, we present a raw data-based deep learning approach that uses thermographic image sequences for the prediction of local porosity. The model takes advantage of the self-attention mechanism that considers not only the thermogram information but also its positional context within the sequence. The model is used to predict porosity in the form of a many-to-one regression. It is trained and tested on a dataset retrieved from the manufacturing of HAYNES282 cuboid specimens. The model results are compared against state-of-the-art thermogram feature-based ML models and artificial neural networks. The raw data model outperforms its feature-based counterparts in terms of prediction scores and, therefore, seems to make better use of the information available in the thermogram data. T2 - 4th Symposium on Materials and Additive Manufacturing CY - Berlin, Germany DA - 12.06.2024 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Machine learning KW - Porosity prediction PY - 2024 AN - OPUS4-62472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Defect prediction on the Base of Thermographic features in Laser Powder Bed Fusion Utilizing Machine Learning Algorithms N2 - Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived. T2 - Additive Manufacturing Benchmarks 2022 CY - Bethesda, MA, USA DA - 14.08.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Machine Learning KW - Defect prediction PY - 2022 AN - OPUS4-55591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermography N2 - Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed. T2 - Thermosense: Thermal Infrared Applications XLIII CY - Online meeting DA - 12.04.2021 KW - SWIR camera KW - Additive manufacturing (AM) KW - Selective laser melting (SLM) KW - Laser beam melting (LBM) KW - In-situ monitoring KW - Infrared thermography PY - 2021 UR - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11743/117430C/Investigation-of-the-thermal-history-of-L-PBF-metal-parts/10.1117/12.2587913.short?SSO=1&tab=ArticleLink AN - OPUS4-52540 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Defect prediction in laser powder bed fusion based on thermographic features utilizing convolutional neural networks N2 - The appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived. T2 - GIMC SIMAI YOUNG 2022 CY - Pavia, Italy DA - 29.09.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-56331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Oster, Simon A1 - Ulbricht, Alexander A1 - Bruno, Giovanni T1 - Review on image registration methods for the quality control in additive manufacturing N2 - A critical challenge in Additive Manufacturing is to ensure the safety and dimensional accuracy of produced parts. Since quality control is made by means of different online and offline imaging techniques (e.g. Thermography, X-ray and Optical Computer Tomography), image registration plays an important role in addressing these challenges. This paper introduces the main motivation, challenges, and research gaps of image registration in Additive Manufacturing. Furthermore, it introduces the main transformations, registration methods, similarity matrices and accuracy measurement. The main aim of the paper is to present a comprehensive review on the available methods for image registration in Additive Manufacturing based on the measurement techniques. Various registration methods, including landmark-based, point cloud-based, image intensity-based, and shape-based techniques, are examined in their applications for quality control, defect detection, and distortion quantification. KW - Image processing KW - Image fusion KW - Computed tomography KW - Computer-aided design KW - Additive manufacturing PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624495 DO - https://doi.org/10.1007/s40964-024-00932-2 SN - 2363-9520 SP - 1 EP - 27 PB - Springer Science and Business Media LLC AN - OPUS4-62449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Oster, Simon A1 - Ulbricht, Alexander A1 - Poka, Konstantin A1 - Bruno, Giovanni T1 - A Comparative Study of Rigid Three-Dimensional Image Registration Methods for Powder Bed Fusion with Laser Beam of Metals Using a Gold Standard Approach N2 - In Additive Manufacturing (AM), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. Given the absence of standardized benchmarks for image registration in AM, we introduce a gold standard for 3D image registration, using a reference base plate beneath the build structure. This gold standard is used to quantify the accuracy of rigid registration, with a proof of concept demonstrated in PBF-LB/M. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. The performance of each registration method is evaluated using measures of alignment accuracy based on the gold standard and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process. KW - Digital volume correlation KW - 3D image registration KW - 3D image processing KW - X-ray computed tomography KW - Computer-aided design KW - Displacement field estimation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627043 DO - https://doi.org/10.1007/s10921-025-01174-0 SN - 1573-4862 VL - 44 IS - 30 SP - 1 EP - 20 PB - Springer AN - OPUS4-62704 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -