Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Lecture Notes in Production Engineering |
| Herausgeber (Verlag) | Springer Nature |
| Seiten | 539-549 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 978-3-031-47394-4 |
| ISBN (Print) | 978-3-031-47393-7 |
| Publikationsstatus | Veröffentlicht - 18 Nov. 2023 |
Publikationsreihe
| Name | Lecture Notes in Production Engineering |
|---|---|
| Band | Part F1764 |
| ISSN (Print) | 2194-0525 |
| ISSN (elektronisch) | 2194-0533 |
Abstract
Rubber extrusion processes involve a high number of control parameters and batch-dependent material fluctuations, leading to lengthy and error-prone testing cycles for new products. To address this challenge and enable a more sustainable production, an artificial neural network-based data mining algorithm is developed that identifies correlations between process parameters and product characteristics. This approach allows for consideration of a larger quantity of non-linear correlations that improve with each new application, enabling more precise temperature control of rubber production. Real-world manufacturing data are used to validate the model, which incorporate batch- and recipe-dependent material variations. The results provide new insights into the complex relationships between process parameters and product characteristics, highlighting the potential of data mining algorithms to drive sustainable production in the rubber extrusion industry. This paper presents the iterative development of the data mining algorithm for rubber extrusion lines and demonstrates its practical application in the industry.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Volkswirtschaftslehre, Ökonometrie und Finanzen (sonstige)
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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- BibTex
- RIS
Lecture Notes in Production Engineering. Springer Nature, 2023. S. 539-549 (Lecture Notes in Production Engineering; Band Part F1764).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines
T2 - An Artificial Neural Network-Based Approach
AU - Lukas, M.
AU - Leineweber, S.
AU - Reitz, B.
AU - Overmeyer, L.
AU - Aschemann, A.
AU - Klie, B.
AU - Giese, U.
N1 - Funding Information: Acknowledgments. The authors gratefully acknowledge the financial support of the German Federal Ministry for Education and Research (BMBF) within the project Digit Rubber (13XP5126A).
PY - 2023/11/18
Y1 - 2023/11/18
N2 - Rubber extrusion processes involve a high number of control parameters and batch-dependent material fluctuations, leading to lengthy and error-prone testing cycles for new products. To address this challenge and enable a more sustainable production, an artificial neural network-based data mining algorithm is developed that identifies correlations between process parameters and product characteristics. This approach allows for consideration of a larger quantity of non-linear correlations that improve with each new application, enabling more precise temperature control of rubber production. Real-world manufacturing data are used to validate the model, which incorporate batch- and recipe-dependent material variations. The results provide new insights into the complex relationships between process parameters and product characteristics, highlighting the potential of data mining algorithms to drive sustainable production in the rubber extrusion industry. This paper presents the iterative development of the data mining algorithm for rubber extrusion lines and demonstrates its practical application in the industry.
AB - Rubber extrusion processes involve a high number of control parameters and batch-dependent material fluctuations, leading to lengthy and error-prone testing cycles for new products. To address this challenge and enable a more sustainable production, an artificial neural network-based data mining algorithm is developed that identifies correlations between process parameters and product characteristics. This approach allows for consideration of a larger quantity of non-linear correlations that improve with each new application, enabling more precise temperature control of rubber production. Real-world manufacturing data are used to validate the model, which incorporate batch- and recipe-dependent material variations. The results provide new insights into the complex relationships between process parameters and product characteristics, highlighting the potential of data mining algorithms to drive sustainable production in the rubber extrusion industry. This paper presents the iterative development of the data mining algorithm for rubber extrusion lines and demonstrates its practical application in the industry.
KW - Artificial Neural Network
KW - Rubber Extrusion
KW - Sustainable Production
UR - http://www.scopus.com/inward/record.url?scp=85178369225&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47394-4_52
DO - 10.1007/978-3-031-47394-4_52
M3 - Contribution to book/anthology
AN - SCOPUS:85178369225
SN - 978-3-031-47393-7
T3 - Lecture Notes in Production Engineering
SP - 539
EP - 549
BT - Lecture Notes in Production Engineering
PB - Springer Nature
ER -