A data-driven approach to linking design features with manufacturing process data for sustainable product development
Li, Jiahang 1; Cazzonelli, Lucas; Höllig, Jacqueline
2; Doellken, Markus
1; Matthiesen, Sven 1
1 Institut für Produktentwicklung (IPEK), Karlsruher Institut für Technologie (KIT)
2 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)
2; Doellken, Markus
1; Matthiesen, Sven 11 Institut für Produktentwicklung (IPEK), Karlsruher Institut für Technologie (KIT)
2 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)
Abstract:
The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data,
unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains,
such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions
significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts
the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship
between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection
and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model
that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach
... mehr
unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains,
such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions
significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts
the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship
between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection
and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model
that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach
... mehr
| Zugehörige Institution(en) am KIT | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) Institut für Produktentwicklung (IPEK) |
| Publikationstyp | Zeitschriftenaufsatz |
| Publikationsjahr | 2026 |
| Sprache | Englisch |
| Identifikator | ISSN: 2212-8271 KITopen-ID: 1000195141 |
| Erschienen in | Procedia CIRP |
| Verlag | Elsevier |
| Band | 142 |
| Seiten | 1 - 6 |
| Bemerkung zur Veröffentlichung | 36th CIRP Design Conference (CIRP Design 2026), Tokyo, March 16th–18, 2026. |
| Vorab online veröffentlicht am | 09.06.2026 |
| Externe Relationen | Siehe auch |
| Nachgewiesen in | Scopus OpenAlex |