[{"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"}],"status":"public","type":"conference","publication":"ECCM21 - Proceedings of the 21st European Conference on Composite Materials","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"],"language":[{"iso":"eng"}],"project":[{"_id":"130","name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten"},{"_id":"137","name":"TRR 285 - Subproject A03"},{"name":"TRR 285 - Project Area A","_id":"131"}],"_id":"62078","user_id":"105344","year":"2024","citation":{"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>","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>","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>.","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} }","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.","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>."},"page":"1252–1259","intvolume":"         3","publication_identifier":{"isbn":["978-2-912985-01-9"]},"title":"Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning","doi":"10.60691/yj56-np80","date_updated":"2026-02-27T06:46:21Z","publisher":"European Society for Composite Materials (ESCM)","date_created":"2025-11-04T12:47:06Z","author":[{"full_name":"Gerritzen, Johannes","id":"105344","orcid":"0000-0002-0169-8602","last_name":"Gerritzen","first_name":"Johannes"},{"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},{"publication_status":"published","quality_controlled":"1","citation":{"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>.","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>.","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>","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>","short":"I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp. 253–524.","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} }"},"page":"253-524","year":"2023","author":[{"full_name":"Gräßler, Iris","id":"47565","orcid":"0000-0001-5765-971X","last_name":"Gräßler","first_name":"Iris"},{"full_name":"Hieb, Michael","id":"72252","last_name":"Hieb","first_name":"Michael"}],"date_created":"2024-03-25T10:16:24Z","publisher":"AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany","date_updated":"2024-03-25T11:05:53Z","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","title":"Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing","type":"conference","publication":"Lectures","status":"public","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"}],"user_id":"5905","department":[{"_id":"152"}],"_id":"52816","language":[{"iso":"eng"}],"keyword":["synthetic training data","machine vision quality gates","deep learning","automated inspection and quality control","production control"]}]