TY - JOUR A1 - Franke, Markus A1 - Wagner, Marcus A1 - Krause, Tom Janis T1 - Data-driven model order reduction with surrogate elements for transient simulations JF - Engineering Computations : International journal for computer-aided engineering and software N2 - Purpose – The purpose of this study is to introduce surrogate elements for static and transient finite element simulations. These elements are designed to replace regions of several conventional solid elements with a single artificial element that possesses a reduced number of degrees of freedoms (dofs). A notable advantage of our surrogate elements is their seamless integration into standard finite element meshes. Design/methodology/approach – The construction of the surrogate elements stiffness and mass matrices is achieved through an optimization process wherein displacements serve as the optimization objective. Moreover, the matrices are designed to possess properties analogous to those of standard finite elements. A particular focus is placed on ensuring that the artificial stiffness matrices are positive semi-definite. Furthermore, artificial degrees of freedom are introduced. Findings – The efficacy of the proposed technique is demonstrated through its application to two different use cases. It is demonstrated that, despite being trained on examples comprising a single surrogate element, the surrogate elements can be employed multiple times within complex and practical models. The degree of accuracy achieved in these applications is noteworthy. Moreover, the proposed method is considerably faster than the fully discretized models. Originality/value – The study expands the field of substructuring and model order reduction by incorporating artificial surrogate elements built by neural networks, which enables seamless integration with standard finite element analysis via positive semi-definite matrices. Furthermore, the introduction of artificial degrees of freedom, which are detached from the computational domain, is proposed. Once trained, the surrogate elements can be utilised in load and support independent scenarios. Y1 - 2025 U6 - https://doi.org/10.1108/EC-06-2024-0511 VL - 2025 PB - Emerald Publishing CY - Leeds ER - TY - JOUR A1 - Wagner, Marcus A1 - Franke, Markus A1 - Krause, Tom Janis A1 - Heinle, Ingo T1 - Data augmentation of material properties for machine learning in industrial production - a case study in an automotive press shop JF - Engineering Computations : International journal for computer-aided engineering and software N2 - In the context of industrial production, the utilisation of data recording and processing techniques is becoming increasingly prevalent across the manufacturing sector. The solutions integrate sensors, facilitate the transmission of data, and enable data-driven decision-making, thereby reducing downtime and optimising quality. However, challenges emerge due to the limited non-transferable data or models between processes. Alterations to the production process can render collected data invalid, resulting in restricted datasets and potential overfitting. To address these issues, techniques such as data augmentation are employed. This study aims to develop a data augmentation methodology applicable in dynamic, data-scarce production environments, enhancing the robustness of regressor predictions. KW - Data augmentation KW - Machine learning KW - Manufacturing KW - Deep drawing Y1 - 2025 U6 - https://doi.org/10.1108/EC-08-2024-0787 PB - Emerald Publishing CY - Leeds ER -