Introducing Artificial Neural Networtks to predict the dimensional and micro-geometrial deviations of additively manufactured parts
Vendittoli, Valentina; Polini, Wilma; Walter, Michael S. J.; Geißelsöder, Stefan (2024)
Procedia CIRP 129, S. 181-186.
DOI: 10.1016/j.procir.2024.10.032
Open Access Peer Reviewed
The dependencies between process parameters and the resulting geometrical accuracy of additively manufactured parts are usually highly non-linear and thus complex to investigate and mathematically quantify. Therefore, the application of artificial intelligence techniques is promising to generate mathematical models that reduce effort and increase the prediction quality. The overall goal is to establish a procedure to automatically determine the optimal settings of the manufacturing process parameters to guarantee the highest geometrical accuracy of parts in additive manufactured production. This paper presents the first step towards this fully automatic procedure – the training and evaluation of a mathematical model based on artificial neural networks to quantify the effects of varying process parameters of a material extrusion process on both macro- and micro-geometrical performances. Therefore, a dataset is established based on the Design of Experiment of an additively manufactured part made from Polylactic Acid filament. The dataset is then used to train an artificial neural network that predicts the dimensional and micro-geometrical deviations of the manufactured parts. Finally, the evaluation of the network's prediction quality and reliability indicate that it is possible to predict the parameters linked to resulting print quality with a mean absolute error from 0.0004 to 0.036.
Using Bayesian Regularized Artificial Neural Networks to Predict the Tensile Strength of Additively Manufactured Polylactic Acid Parts
Vendittoli, Valentina; Polini, Wilma; Walter, Michael S. J.; Geißelsöder, Stefan (2024)
Applied Sciences 14, 3184 (8).
DOI: 10.3390/app14083184
Open Access Peer Reviewed
Additive manufacturing has transformed the production process by enabling the construction of components in a layer-by-layer approach. This study integrates Artificial Neural Networks to explore the nuanced relationship between process parameters and mechanical performance in Fused Filament Fabrication. Using a fractional Taguchi design, seven key process parameters are systematically varied to provide a robust dataset for model training. The resulting model confirms its accuracy in predicting tensile strength. In particular, the mean squared error is 0.002, and the mean absolute error is 0.024. These results significantly advance the understanding of 3D manufactured parts, shedding light on the intricate dynamics between process nuances and mechanical outcomes. Furthermore, they underscore the transformative role of machine learning in precision-driven quality prediction and optimization in additive manufacturing.
Kopplung von KI, Strömungssimulation und Strömungsmessung
Zacharias, Konstantin; Welsch, Dennis; Geißelsöder, Stefan; Buchele, Alexander (2023)
mfund Konferenz 2023, Berlin.
Signal analysis and classification of interictal epileptiform discharges from EEG with machine learning
Stiehl, Annika; Geißelsöder, Stefan; Anselstetter, Fabienne; Bornfleth, Harald; Ille, Nicole; Uhl, Christian (2023)
Stiehl, Annika; Geißelsöder, Stefan; Anselstetter, Fabienne; Bornfleth, Harald...
BMT 2023, 26.09. - 28.09.2023, Duisburg.
DOI: 10.1515/bmte-2023-2001
Peer Reviewed
Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges
Stiehl, Annika; Flammer, M; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald; Geißelsöder, Stefan; Uhl, Christian (2023)
Stiehl, Annika; Flammer, M; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald...
2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece, S. 1-5.
DOI: 10.1109/ICASSPW59220.2023.10193167
Peer Reviewed
Industrielle Anwendungen
Geißelsöder, Stefan (2023)
Knowledge Science – Fallstudien. Springer Vieweg, Wiesbaden, S. 193-205.
DOI: 10.1007/978-3-658-41155-8_9
Different montages and dimension reduction methods for EEG signal analysis of Interictal Epileptic Discharges
Stiehl, Annika; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald; Geißelsöder, Stefan; Uhl, Christian (2022)
Stiehl, Annika; Anselstetter, Fabienne; Ille, Nicole; Bornfleth, Harald...
Abstracts of the 2022 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE)Societies for Biomedical Engineering 67 (S1), S. 88.
DOI: 10.1515/bmt-2022-2001
Open Access Peer Reviewed
Dimension reduction methods, persistent homology ans machine learning for EEG signal analysis of Interictal Epileptic Discharges
Stiehl, Annika; Geißelsöder, Stefan; Ille, Nicole; Anselstetter, Fabienne; Bornfleth, Harald; Uhl, Christian (2022)
Stiehl, Annika; Geißelsöder, Stefan; Ille, Nicole; Anselstetter, Fabienne...
Proceedings of the Workshop Biosignale 2022 (24. - 26.08.2022).
DOI: 10.48550/arXiv.2502.12814
Peer Reviewed
On the intelligence of interacting autonomous robots and agents
Geißelsöder, Stefan (2022)
4th International Conference Business Meets Technology 2022.
DOI: 10.4995/BMT2022.2022.15555
Open Access
ExDRa: Exploratory Data Science on Federated Raw Data
Baunsgaard, Sebastian ; Boehm, Matthias ; Chaudhary , Ankit; Derakhshan, Behrouz ; Geißelsöder, Stefan; Grulich, Philipp M. ; Hildebrand, Michael; Innerebner, Kevin ; Markl, Volker; Neubauer, Claus; Osterburg, Sarah ; Ovcharenko, Olga ; Redyuk, Sergey ; Rieger, Tobias ; Mahdiraji, Alireza Rezaei ; Wrede, Sebastian Benjamin ; Zeuch, Steffen (2021)
Baunsgaard, Sebastian ; Boehm, Matthias ; Chaudhary , Ankit; Derakhshan, Behrouz ...
SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data, S. 2450–2463.
Peer Reviewed
Vorrichtung und Verfahren zur Erkennung von Anomalien in einer industriellen Anlage zur Ausführung eines Produktionsprozesses
Geißelsöder, Stefan; Hitzel, Klaus-Peter; Dilek, Hakan; Hertlein, Christian Klaus; Klose, Marcel Mathias; Tauber, Christian (2020)
Geißelsöder, Stefan; Hitzel, Klaus-Peter; Dilek, Hakan; Hertlein, Christian Klaus...
Europäisches Patent. Internationale Veröffentlichungsnummer: WO2022/069258/A1.
Model-independent search for neutrino sources with the ANTARES neutrino telescope
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., .; Geißelsöder, Stefan; et al., . (2020)
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., ....
Astroparticle Physics
114, S. 35-47.
DOI: 10.1016/j.astropartphys.2019.06.003
Peer Reviewed
Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope
Aiello, S.; Akrame, S.E.; Ameli, F.; Anassontzis, E. G.; André, M; Androulakis, G.; et al., .; Geißelsöder, Stefan; et al., . (2018)
Aiello, S.; Akrame, S.E.; Ameli, F.; Anassontzis, E. G.; André, M; Androulakis, G....
Journal of Instrumentation 13, P05035.
Open Access Peer Reviewed
Deep Learning mit unbalancierten Datensätzen
Geißelsöder, Stefan (2018)
Verhandlungen der Deutschen Physikalischen Gesellschaft e.V.. Würzburg 2018.
A polarized fast radio burst at low Galactic latitude
Petroff, E.; Burke-Spolaor, S.; Keane, E. F.; McLaughlin, M. A.; Miller , R.; Andreoni, I.; et al., ..; Geißelsöder, Stefan; et al., . (2017)
Petroff, E.; Burke-Spolaor, S.; Keane, E. F.; McLaughlin, M. A.; Miller , R....
Monthly Notices of the Royal Astronomical Society 469 (4), S. 4465-4482.
DOI: 10.1093/mnras/stx1098
Open Access Peer Reviewed
Results from the search for dark matter in the Milky Way with 9 years of data of the ANTARES neutrino telescope
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., .; Geißelsöder, Stefan; et al., . (2017)
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., ....
Physics Letters B 769, S. 249-254.
Peer Reviewed
Time-dependent search for neutrino emission from X-ray binaries with the ANTARES telescope
Albert, A.; André, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; Avgitas, T.; et al., .; Geißelsöder, Stefan; et al., . (2017)
Albert, A.; André, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; Avgitas, T.; et al., ....
Journal of Cosmology and Astroparticle Physics 04(2017), 019.
DOI: 10.1088/1475-7516/2017/04/019
Peer Reviewed
Search for high-energy neutrinos from bright GRBs with ANTARES
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., .; Geißelsöder, Stefan; et al., . (2017)
Albert, A.; André, M.; Anghinolfi, M; Anton, G; Ardid, Miguel; Aubert, J.-J.; et al., ....
Monthly Notices of the Royal Astronomical Society 469 (1), S. 906-915.
DOI: 10.1093/mnras/stx902
Peer Reviewed
Sperm whale long-range echolocation sounds revealed by ANTARES, a deep-sea neutrino telescope
André, M.; Caballé, A.; van der Schaar, M.; Solsona Berga, Alba; Houegnigan, Ludwig; Zaug, Serge; Sánchez Marrero, Antonio Sánchez Marrero; Castell, J.V.; Solé, M; Vila, F; Djokić, Divna; Adrián-Martínez, S.; Albert, A.; et al., .; Geißelsöder, Stefan; et al., . (2017)
André, M.; Caballé, A.; van der Schaar, M.; Solsona Berga, Alba; Houegnigan, Ludwig...
Scientific Reports 7, 45517.
DOI: 10.1038/srep45517
Open Access Peer Reviewed
Deep Learning für Neutrinoteleskope
Geißelsöder, Stefan (2017)
Verhandlungen der Deutschen Physikalischen Gesellschaft e.V.. Münster 2017.
Prof. Dr. Stefan Geißelsöder
Hochschule Ansbach
Hochschule Ansbach - AN[ki]T Zentrum für angewandte KI und Transfer
Residenzstr. 8
91522 Ansbach
T 0981 4877 415 stefan.geisselsoeder[at]hs-ansbach.de