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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.09690 |
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| _version_ | 1866915666383077376 |
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| author | Li, Jiahang Cazzonelli, Lucas Höllig, Jacqueline Doellken, Markus Matthiesen, Sven |
| author_facet | Li, Jiahang Cazzonelli, Lucas Höllig, Jacqueline Doellken, Markus Matthiesen, Sven |
| contents | 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 opens new possibilities for sustainable product development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A data-driven approach to linking design features with manufacturing process data for sustainable product development Li, Jiahang Cazzonelli, Lucas Höllig, Jacqueline Doellken, Markus Matthiesen, Sven Machine Learning 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 opens new possibilities for sustainable product development. |
| title | A data-driven approach to linking design features with manufacturing process data for sustainable product development |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.09690 |