---
_id: '57182'
abstract:
- lang: eng
  text: '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:
- first_name: Manuel
  full_name: Ott, Manuel
  id: '44204'
  last_name: Ott
- first_name: Niclas
  full_name: Meihöfener, Niclas
  id: '38611'
  last_name: Meihöfener
- first_name: Iryna
  full_name: Mozgova, Iryna
  id: '95903'
  last_name: Mozgova
citation:
  ama: 'Ott M, Meihöfener N, Mozgova I. An approach to use generic data sets for neural
    networks in product designs through geometric abstraction via primitives. In:
    Anisic Z, Forza C, eds. <i>Proceedings of the 11. Conference on Mass Customization
    and Personalization (MCP)</i>. Faculty of Technical Science, Department of Industrial
    Engineering and Management ; 2024.'
  apa: Ott, M., Meihöfener, N., &#38; Mozgova, I. (2024). An approach to use generic
    data sets for neural networks in product designs through geometric abstraction
    via primitives. In Z. Anisic &#38; C. Forza (Eds.), <i>Proceedings of the 11.
    Conference on Mass Customization and Personalization (MCP)</i>. Faculty of Technical
    Science, Department of Industrial Engineering and Management .
  bibtex: '@inproceedings{Ott_Meihöfener_Mozgova_2024, place={Novi Sad, Serbia}, title={An
    approach to use generic data sets for neural networks in product designs through
    geometric abstraction via primitives}, booktitle={Proceedings of the 11. Conference
    on Mass Customization and Personalization (MCP)}, publisher={Faculty of Technical
    Science, Department of Industrial Engineering and Management }, author={Ott, Manuel
    and Meihöfener, Niclas and Mozgova, Iryna}, editor={Anisic, Zoran  and Forza,
    Cipriano}, year={2024} }'
  chicago: 'Ott, Manuel, Niclas Meihöfener, and Iryna Mozgova. “An Approach to Use
    Generic Data Sets for Neural Networks in Product Designs through Geometric Abstraction
    via Primitives.” In <i>Proceedings of the 11. Conference on Mass Customization
    and Personalization (MCP)</i>, edited by Zoran  Anisic and Cipriano Forza. Novi
    Sad, Serbia: Faculty of Technical Science, Department of Industrial Engineering
    and Management , 2024.'
  ieee: M. Ott, N. Meihöfener, and I. Mozgova, “An approach to use generic data sets
    for neural networks in product designs through geometric abstraction via primitives,”
    in <i>Proceedings of the 11. Conference on Mass Customization and Personalization
    (MCP)</i>, Novi Sad, Serbia, 2024.
  mla: Ott, Manuel, et al. “An Approach to Use Generic Data Sets for Neural Networks
    in Product Designs through Geometric Abstraction via Primitives.” <i>Proceedings
    of the 11. Conference on Mass Customization and Personalization (MCP)</i>, edited
    by Zoran  Anisic and Cipriano Forza, Faculty of Technical Science, Department
    of Industrial Engineering and Management , 2024.
  short: 'M. Ott, N. Meihöfener, I. Mozgova, in: Z. Anisic, C. Forza (Eds.), Proceedings
    of the 11. Conference on Mass Customization and Personalization (MCP), Faculty
    of Technical Science, Department of Industrial Engineering and Management , Novi
    Sad, Serbia, 2024.'
conference:
  end_date: 2024-09-27
  location: Novi Sad, Serbia
  name: 11. Conference on Mass Customization and Personalization (MCP)
  start_date: 2024-09-24
date_created: 2024-11-18T10:20:34Z
date_updated: 2024-11-28T06:45:13Z
department:
- _id: '741'
- _id: '144'
editor:
- first_name: 'Zoran '
  full_name: 'Anisic, Zoran '
  last_name: Anisic
- first_name: Cipriano
  full_name: Forza, Cipriano
  last_name: Forza
language:
- iso: eng
place: Novi Sad, Serbia
publication: Proceedings of the 11. Conference on Mass Customization and Personalization
  (MCP)
publication_identifier:
  isbn:
  - 978-86-6022-686-2
publication_status: published
publisher: 'Faculty of Technical Science, Department of Industrial Engineering and
  Management '
status: public
title: An approach to use generic data sets for neural networks in product designs
  through geometric abstraction via primitives
type: conference
user_id: '44204'
year: '2024'
...