Investigations on Explainable Artificial Intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing S Meister, M Wermes, J Stüve, RM Groves Composites Part B: Engineering 224, 109160, 2021 | 79 | 2021 |
Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set S Meister, N Möller, J Stüve, RM Groves Journal of Intelligent Manufacturing 32 (6), 1767-1789, 2021 | 60 | 2021 |
Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process RMG Sebastian Meister, Mahdieu A. M. Wermes, Jan Stüve Journal of Intelligent Manufacturing, 2021 | 55* | 2021 |
Cross-evaluation of a parallel operating SVM–CNN classifier for reliable internal decision-making processes in composite inspection S Meister, M Wermes, J Stueve, RM Groves Journal of Manufacturing Systems 60, 620-639, 2021 | 26 | 2021 |
Automated, quality assured and high volume oriented production of fiber metal laminates (FML) for the next generation of passenger aircraft fuselage shells H Ucan, J Scheller, C Nguyen, D Nieberl, T Beumler, A Haschenburger, ... Science and Engineering of Composite Materials 26 (1), 502-508, 2019 | 21 | 2019 |
Algorithm assessment for layup defect segmentation from laser line scan sensor based image data S Meister, MAM Wermes, J Stüve, RM Groves Sensors and smart structures technologies for civil, mechanical, and …, 2020 | 14 | 2020 |
Performance evaluation of CNN and R-CNN based line by line analysis algorithms for fibre placement defect classification S Meister, M Wermes Production Engineering 17 (3), 391-406, 2023 | 10 | 2023 |
Reflectivity and emissivity analysis of thermoplastic CFRP for optimising Xenon heating and thermographic measurements S Meister, A Kolbe, RM Groves Composites Part A: Applied Science and Manufacturing 158, 106972, 2022 | 10 | 2022 |
Imaging sensor data modelling and evaluation based on optical composite characteristics: Investigation of data quality for inline inspection S Meister, L Grundhöfer, J Stüve, RM Groves The International Journal of Advanced Manufacturing Technology 116 (11 …, 2021 | 9 | 2021 |
Optical material characterisation of prepreg CFRP for improved composite inspection S Meister, J Stüve, RM Groves Applied Composite Materials 29 (2), 871-887, 2022 | 6 | 2022 |
Explainability of deep learning classifier decisions for optical detection of manufacturing defects in the automated fiber placement process S Meister, MAM Wermes, J Stüve, RM Groves Automated Visual Inspection and Machine Vision IV 11787, 31-37, 2021 | 6 | 2021 |
Automated defect analysis using optical sensing and explainable artificial intelligence for fibre layup processes in composite manufacturing S Meister Delft University of Technology, 2022 | 5 | 2022 |
Verfahren der INLINE-Qualitätssicherung und der zerstörungsfreien Prüfung innerhalb der Fertigungslinie von Faser-Metall-Laminaten H Apmann, M Mayer, K Fortkamp, A Haschenburger, C Krombholz, ... Deutsche Gesellschaft für Luft-und Raumfahrt-Lilienthal-Oberth eV, 2017 | 4 | 2017 |
Enhancements of an inline QA system for fiber layup processes S Meister, S Kaestner, C Krombholz | 2 | 2018 |
Konzeptionierung und Implementierung einer bildbasierten online-Qualitätssicherung für den automatisierten Faserlegeprozess S Meister, C Krombholz | 2 | 2017 |
Software for inline quality assurance within automated fibre layup processes S Meister | 1 | 2018 |
DHiiP-AIR–Ablage-und Integrationstechnologien für RTM-Prozesse–Schlussbericht C Krombholz, F Behrens, DPP Delisle, B Denker, Y Grohmann, R Hein, ... | | 2024 |
Ressourceneffizientes Fliegen durch intelligente Qualitätssicherung im Fertigungsprozess S Meister, C Brauer Bitkom Roundtable Digitale Luftfahrt, 2022 | | 2022 |
Mit künstlicher Intelligenz den Fehlern auf der Spur S Meister | | 2021 |
Erklärbarkeit von Entscheidungsprozessen Maschineller Lernverfahren am Beispiel der optischen Inspektion im Faserverbundleichtbau J Brüst, S Meister, C Brauer Fachhochschule Wedel, 2021 | | 2021 |