[{"title":"Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning","doi":"10.60691/yj56-np80","publisher":"European Society for Composite Materials (ESCM)","date_updated":"2026-02-27T06:46:21Z","volume":3,"date_created":"2025-11-04T12:47:06Z","author":[{"last_name":"Gerritzen","orcid":"0000-0002-0169-8602","full_name":"Gerritzen, Johannes","id":"105344","first_name":"Johannes"},{"last_name":"Hornig","full_name":"Hornig, Andreas","first_name":"Andreas"},{"first_name":"Peter","last_name":"Winkler","full_name":"Winkler, Peter"},{"last_name":"Gude","full_name":"Gude, Maik","first_name":"Maik"}],"year":"2024","page":"1252–1259","intvolume":"         3","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} }","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.","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>.","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>","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>.","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>."},"publication_identifier":{"isbn":["978-2-912985-01-9"]},"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"}],"_id":"62078","project":[{"_id":"130","name":"TRR 285:  Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen Prozessketten"},{"name":"TRR 285 - Subproject A03","_id":"137"},{"_id":"131","name":"TRR 285 - Project Area A"}],"user_id":"105344","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","publication":"ECCM21 - Proceedings of the 21st European Conference on Composite Materials","type":"conference"},{"type":"dissertation","status":"public","file":[{"date_updated":"2024-01-11T09:24:01Z","creator":"dcj","date_created":"2024-01-11T09:24:01Z","file_size":7694237,"access_level":"closed","file_id":"50451","file_name":"01_Dissertation_CJDP_7065653_V1.pdf","content_type":"application/pdf","success":1,"relation":"main_file"}],"abstract":[{"lang":"eng","text":"The importance of fiber-reinforced plastics for lightweight construction applications is steadily increasing due to their outstanding weight-specific property values. However, a decisive disadvantage of these composite materials has so far been the high material and process costs, which is why fiber-reinforced plastics are almost exclusively used in small to medium-sized series. Optimization of manufacturing methods is of great importance to reduce the production cost. In this study, two concepts are proposed that can optimize vacuum assisted light resin transfer molding (VA-LRTM) further, leading to a possibility of fully automatic process. Conventional VA-LRTM methods are used to produce complex fiber-reinforced plastics (FRP) and hybrid components. Traditional molds used to produce components via VA-LRTM are sealed using polymer materials to prevent the leakage of matrix system. The seals undergo tremendous amounts of thermal, chemical, and mechanical loadings. Thus, sealings must be replaced in short intervals. In the current study, a concept where sealing is achieved by accelerating the curing of matrix system itself with the help of heating elements and catalysts resulting in a self-sealing approach is proposed. Another concern is mold surface contamination during component production. To address this, a modified automatic cleaning technique based on ultrasonic cleaning was proposed which can be integrated into the production line with minimum modification. Both the proposed concepts were validated and optimized using experiments, simulations, and analytical approaches by producing metal-FRP hybrid shafts."}],"department":[{"_id":"9"},{"_id":"149"},{"_id":"321"}],"user_id":"49504","_id":"50449","file_date_updated":"2024-01-11T09:24:01Z","language":[{"iso":"eng"}],"keyword":["fiber-reinforced plastics","resin transfer molding","composites"],"ddc":["670"],"has_accepted_license":"1","citation":{"ama":"Chalicheemalapalli Jayasankar D. <i>Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>.; 2023.","chicago":"Chalicheemalapalli Jayasankar, Deviprasad. <i>Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>, 2023.","ieee":"D. Chalicheemalapalli Jayasankar, <i>Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>. 2023.","apa":"Chalicheemalapalli Jayasankar, D. (2023). <i>Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>.","short":"D. Chalicheemalapalli Jayasankar, Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques, 2023.","bibtex":"@book{Chalicheemalapalli Jayasankar_2023, title={Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques}, author={Chalicheemalapalli Jayasankar, Deviprasad}, year={2023} }","mla":"Chalicheemalapalli Jayasankar, Deviprasad. <i>Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques</i>. 2023."},"year":"2023","supervisor":[{"last_name":"Tröster","full_name":"Tröster, Thomas","first_name":"Thomas"},{"last_name":"Bremser","full_name":"Bremser, Wolfgang","first_name":"Wolfgang"}],"author":[{"id":"49504","full_name":"Chalicheemalapalli Jayasankar, Deviprasad","orcid":"https://orcid.org/ 0000-0002-3446-2444","last_name":"Chalicheemalapalli Jayasankar","first_name":"Deviprasad"}],"date_created":"2024-01-11T09:28:04Z","date_updated":"2024-03-26T09:18:31Z","title":"Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques"}]