@inproceedings{64786,
  author       = {{Müller, Laura and Meihöfener, Niclas and Siemoneit, Johannes Gabriel and Mozgova, Iryna}},
  booktitle    = {{Engineering Education for Sustainable Development (EESD2025)}},
  title        = {{{Introduction of electronic lab notebooks in engineering education - opportunities for a cultural change}}},
  doi          = {{https://doi.org/10.71779/776}},
  year         = {{2025}},
}

@inproceedings{57182,
  abstract     = {{Generative design suggestions and topology optimizations can help to reduce iterative process loops between calculation and design departments during product development processes. However, precise topology optimizations are computationally intensive, while generative designs benefit from swift suggestions to address design problems efficiently. Using artificial neural networks (ANN) can address this contrast of pre-defined aims by predicting topology-optimized designs, thereby combining both advantageous features. However, a challenge in Mass Customization is, that ANN are usually trained on specific geometries, making transfer to other applications impractical or requiring the creation of new datasets, which is economically unfeasible. Authors have already demonstrated a solution in other publications: dividing a geometry into geometric primitives like cuboids to perform abstraction. An ANN can then be trained to recognize optimized cuboids, which can be assembled back into a complete geometry, comparable to the finite element methods, which divide geometries of parts in finite elements enable mechanical property calculation. This publication aims to illustrate the steps of the approach in which the complete geometry of a part is segmented into these primitives, and the benefits obtained. Various methods will be explored, including automated workflows on modern low-code platforms, to enable generalized use.}},
  author       = {{Ott, Manuel and Meihöfener, Niclas and Mozgova, Iryna}},
  booktitle    = {{Proceedings of the 11. Conference on Mass Customization and Personalization (MCP)}},
  editor       = {{Anisic, Zoran  and Forza, Cipriano}},
  isbn         = {{978-86-6022-686-2}},
  location     = {{Novi Sad, Serbia}},
  publisher    = {{Faculty of Technical Science, Department of Industrial Engineering and Management }},
  title        = {{{An approach to use generic data sets for neural networks in product designs through geometric abstraction via primitives}}},
  year         = {{2024}},
}

@inproceedings{46957,
  abstract     = {{Modern companies often face various challenges in concept development of products or systems. Design engineers prepare initial concepts as 3D models. These are then simulated by computational engineers. If requirements are not met, this necessitates an iterative process that runs between the design and computation departments until a valid concept is created. Design methods such as topology optimization are often used here. The upcoming result is then attempted to be adapted to certain manufacturing processes. These iteration loops can sometimes take a very long time, since the model construction and structural optimization generate large computational efforts. The present work shows on an example a methodical approach, which represents a first proof of concept, to solving this problem, including a description of methods and techniques, as well as possible problems in a detailed analysis concerning training data for neural networks and their abstraction capabilities. It is evident that additional research work needs to be conducted for further utilization in order to address all arising questions.}},
  author       = {{Ott, Manuel and Meihöfener, Niclas and Mozgova, Iryna}},
  booktitle    = {{Proceedings of the 34rd Annual International Solid Freeform Fabrication Symposium 2023}},
  editor       = {{Ott, Manuel}},
  location     = {{Austin, Texas, United States}},
  title        = {{{Methodical Approach to Reducing Design Time by using Neural Networks in Early Stages of Concept Development}}},
  year         = {{2023}},
}