[{"type":"conference","publication":"ECCM21 - Proceedings of the 21st European Conference on Composite Materials","status":"public","abstract":[{"text":"Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. ","lang":"eng"}],"user_id":"105344","project":[{"name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten","_id":"130"},{"name":"TRR 285 - Subproject A03","_id":"137"},{"name":"TRR 285 - Project Area A","_id":"131"}],"_id":"62078","language":[{"iso":"eng"}],"keyword":["Direct parameter identification","Machine learning","Convolutional neural networks","Strain rate dependency","Fiber reinforced plastics","woven composites","segmentation","synthetic training data","x-ray computed tomography"],"publication_identifier":{"isbn":["978-2-912985-01-9"]},"citation":{"bibtex":"@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}, volume={3}, DOI={<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>}, booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials}, publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024}, pages={1252–1259} }","mla":"Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, vol. 3, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>.","short":"J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of the 21st European Conference on Composite Materials, European Society for Composite Materials (ESCM), 2024, pp. 1252–1259.","apa":"Gerritzen, J., Hornig, A., Winkler, P., &#38; Gude, M. (2024). Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, <i>3</i>, 1252–1259. <a href=\"https://doi.org/10.60691/yj56-np80\">https://doi.org/10.60691/yj56-np80</a>","ama":"Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning. In: <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>. Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:<a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>","ieee":"J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning,” in <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, 2024, vol. 3, pp. 1252–1259, doi: <a href=\"https://doi.org/10.60691/yj56-np80\">10.60691/yj56-np80</a>.","chicago":"Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive Models Using Machine Learning.” In <i>ECCM21 - Proceedings of the 21st European Conference on Composite Materials</i>, 3:1252–1259. European Society for Composite Materials (ESCM), 2024. <a href=\"https://doi.org/10.60691/yj56-np80\">https://doi.org/10.60691/yj56-np80</a>."},"intvolume":"         3","page":"1252–1259","year":"2024","date_created":"2025-11-04T12:47:06Z","author":[{"first_name":"Johannes","full_name":"Gerritzen, Johannes","id":"105344","last_name":"Gerritzen","orcid":"0000-0002-0169-8602"},{"full_name":"Hornig, Andreas","last_name":"Hornig","first_name":"Andreas"},{"last_name":"Winkler","full_name":"Winkler, Peter","first_name":"Peter"},{"first_name":"Maik","last_name":"Gude","full_name":"Gude, Maik"}],"volume":3,"date_updated":"2026-02-27T06:46:21Z","publisher":"European Society for Composite Materials (ESCM)","doi":"10.60691/yj56-np80","title":"Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning"},{"publication":"Lectures","type":"conference","abstract":[{"text":"Manufacturing companies face the challenge of reaching required quality standards. Using\r\noptical sensors and deep learning might help. However, training deep learning algorithms\r\nrequire large amounts of visual training data. Using domain randomization to generate synthetic\r\nimage data can alleviate this bottleneck. This paper presents the application of synthetic\r\nimage training data for optical quality inspections using visual sensor technology. The results\r\nshow synthetically generated training data are appropriate for visual quality inspections.","lang":"eng"}],"status":"public","_id":"52816","department":[{"_id":"152"}],"user_id":"5905","keyword":["synthetic training data","machine vision quality gates","deep learning","automated inspection and quality control","production control"],"language":[{"iso":"eng"}],"quality_controlled":"1","publication_status":"published","year":"2023","page":"253-524","citation":{"apa":"Gräßler, I., &#38; Hieb, M. (2023). Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing. <i>Lectures</i>, 253–524. <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">https://doi.org/10.5162/smsi2023/d7.4</a>","mla":"Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing.” <i>Lectures</i>, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524, doi:<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>.","bibtex":"@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>}, booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524} }","short":"I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524.","ama":"Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing. In: <i>Lectures</i>. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:<a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>","chicago":"Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing.” In <i>Lectures</i>, 253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023. <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">https://doi.org/10.5162/smsi2023/d7.4</a>.","ieee":"I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing,” in <i>Lectures</i>, Nuremberg, 2023, pp. 253–524, doi: <a href=\"https://doi.org/10.5162/smsi2023/d7.4\">10.5162/smsi2023/d7.4</a>."},"publisher":"AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany","date_updated":"2024-03-25T11:05:53Z","date_created":"2024-03-25T10:16:24Z","author":[{"first_name":"Iris","full_name":"Gräßler, Iris","id":"47565","orcid":"0000-0001-5765-971X","last_name":"Gräßler"},{"id":"72252","full_name":"Hieb, Michael","last_name":"Hieb","first_name":"Michael"}],"title":"Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing","conference":{"location":"Nuremberg","end_date":"2023-05-11","start_date":"2023-05-08","name":"SMSI 2023. Sensor and Measurement Science International"},"doi":"10.5162/smsi2023/d7.4"},{"language":[{"iso":"eng"}],"keyword":["Location awareness","Coils","Couplings","Nonuniform electric fields","Magnetic separation","Neural networks","Training data"],"user_id":"38240","department":[{"_id":"59"},{"_id":"485"}],"project":[{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"22480","status":"public","abstract":[{"lang":"eng","text":"In this publication important aspects for the implementation of inductive locating are explained. The miniaturized sensor platform called Sens-o-Spheres is used as an application of this locating method. The sensor platform is applied in bioreactors in order to obtain the environmental parameters, which makes a localization by magnetic fields necessary. Since the properties of magnetic fields in the localization area are very different from the wave characteristics, the principle of inductive localization is investigated in this publication and explained by using electrical equivalent circuit diagrams. Thereby, inductive localization uses the coupling or the mutual inductivities between coils, which is noticeable by an induced voltage. Therefore some properties and procedures are explained to extract the location of Sens-o-Spheres or other industrial sensor platforms from the couplings of the coils. One method calculates the location from an adapted ratio calculation and the other method uses neural networks and stochastic filters to obtain the results. In the end, these results are evaluated and compared."}],"type":"conference","publication":"22nd IEEE International Conference on Industrial Technology (ICIT)","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9453609"}],"doi":"10.1109/icit46573.2021.9453609","conference":{"location":"Valencia, Spain ","end_date":"2021-03-12","start_date":"2021-03-10","name":"22nd IEEE International Conference on Industrial Technology (ICIT)"},"title":"Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses","author":[{"first_name":"Sven","last_name":"Lange","full_name":"Lange, Sven","id":"38240"},{"full_name":"Schröder, Dominik","last_name":"Schröder","first_name":"Dominik"},{"first_name":"Christian","last_name":"Hedayat","full_name":"Hedayat, Christian"},{"first_name":"Harald","last_name":"Kuhn","full_name":"Kuhn, Harald"},{"last_name":"Hilleringmann","full_name":"Hilleringmann, Ulrich","first_name":"Ulrich"}],"date_created":"2021-06-20T23:25:54Z","date_updated":"2022-01-06T06:55:33Z","publisher":"IEEE","citation":{"ama":"Lange S, Schröder D, Hedayat C, Kuhn H, Hilleringmann U. Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses. In: <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>","ieee":"S. Lange, D. Schröder, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses,” in <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, Valencia, Spain , 2021.","chicago":"Lange, Sven, Dominik Schröder, Christian Hedayat, Harald Kuhn, and Ulrich Hilleringmann. “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE, 2021. <a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">https://doi.org/10.1109/icit46573.2021.9453609</a>.","apa":"Lange, S., Schröder, D., Hedayat, C., Kuhn, H., &#38; Hilleringmann, U. (2021). Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses. In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>.  Valencia, Spain : IEEE. <a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">https://doi.org/10.1109/icit46573.2021.9453609</a>","short":"S. Lange, D. Schröder, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE,  Valencia, Spain , 2021.","bibtex":"@inproceedings{Lange_Schröder_Hedayat_Kuhn_Hilleringmann_2021, place={ Valencia, Spain }, title={Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}, DOI={<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>}, booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)}, publisher={IEEE}, author={Lange, Sven and Schröder, Dominik and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021} }","mla":"Lange, Sven, et al. “Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses.” <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453609\">10.1109/icit46573.2021.9453609</a>."},"place":" Valencia, Spain ","year":"2021","publication_status":"published","publication_identifier":{"isbn":["9781728157306"]}},{"keyword":["clean speech training data","iterative methods","iterative speech enhancement","Kalman filter","Kalman filters","Kalman-LM-iterative algorithm","line spectral pair parameters","log-spectral distance","marginalized particle filter","noise level","nonlinear dynamic state speech model","particle filtering (numerical methods)","single channel speech enhancement","SNR gains","speech enhancement","speech samples"],"language":[{"iso":"eng"}],"_id":"11943","user_id":"44006","department":[{"_id":"54"}],"abstract":[{"lang":"eng","text":"A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved"}],"status":"public","type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","title":"Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf"}],"doi":"10.1109/ICASSP.2006.1660058","oa":"1","date_updated":"2022-01-06T06:51:12Z","date_created":"2019-07-12T05:31:15Z","author":[{"first_name":"Stefan","full_name":"Windmann, Stefan","last_name":"Windmann"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"volume":1,"year":"2006","citation":{"ama":"Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I. doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 1:I, 2006. <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>.","ieee":"S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p. I.","apa":"Windmann, S., &#38; Haeb-Umbach, R. (2006). Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>","bibtex":"@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006}, pages={I} }","short":"S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol. 1, 2006, p. I, doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>."},"intvolume":"         1","page":"I"},{"volume":9,"date_created":"2019-07-12T05:28:04Z","author":[{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"oa":"1","date_updated":"2022-01-06T06:51:08Z","doi":"10.1109/89.906003","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf"}],"title":"Automatic generation of phonetic regression class trees for MLLR adaptation","issue":"3","intvolume":"         9","page":"299-302","citation":{"apa":"Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>, <i>9</i>(3), 299–302. <a href=\"https://doi.org/10.1109/89.906003\">https://doi.org/10.1109/89.906003</a>","short":"R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.","bibtex":"@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression class trees for MLLR adaptation}, volume={9}, DOI={<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>}, number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach, Reinhold}, year={2001}, pages={299–302} }","mla":"Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>, vol. 9, no. 3, 2001, pp. 299–302, doi:<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>.","ama":"Haeb-Umbach R. Automatic generation of phonetic regression class trees for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>. 2001;9(3):299-302. doi:<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>","ieee":"R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for MLLR adaptation,” <i>IEEE Transactions on Speech and Audio Processing</i>, vol. 9, no. 3, pp. 299–302, 2001.","chicago":"Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i> 9, no. 3 (2001): 299–302. <a href=\"https://doi.org/10.1109/89.906003\">https://doi.org/10.1109/89.906003</a>."},"year":"2001","department":[{"_id":"54"}],"user_id":"44006","_id":"11778","language":[{"iso":"eng"}],"keyword":["acoustic space","adaptation experiments","automatic generation","bottom-up clustering","broad phonetic class regression trees","correlation criterion","correlation methods","maximum likelihood estimation","maximum likelihood linear regression based speaker adaptation","MLLR adaptation","pattern clustering","phonetic regression class trees","speaker-independent training data","speech recognition","speech units","statistical analysis","trees (mathematics)"],"publication":"IEEE Transactions on Speech and Audio Processing","type":"journal_article","status":"public","abstract":[{"text":"In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree","lang":"eng"}]}]