Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach

Research output: Chapter in book/report/conference proceedingContribution to book/anthologyResearchpeer review

Authors

  • M. Lukas
  • S. Leineweber
  • B. Reitz
  • L. Overmeyer
  • A. Aschemann
  • B. Klie
  • U. Giese

External Research Organisations

  • German Institute of Rubber Technology (DIK e.V.)
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Details

Original languageEnglish
Title of host publicationLecture Notes in Production Engineering
PublisherSpringer Nature
Pages539-549
Number of pages11
ISBN (electronic)978-3-031-47394-4
ISBN (print)978-3-031-47393-7
Publication statusPublished - 18 Nov 2023

Publication series

NameLecture Notes in Production Engineering
VolumePart F1764
ISSN (Print)2194-0525
ISSN (electronic)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.

Keywords

    Artificial Neural Network, Rubber Extrusion, Sustainable Production

ASJC Scopus subject areas

Cite this

Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach. / Lukas, M.; Leineweber, S.; Reitz, B. et al.
Lecture Notes in Production Engineering. Springer Nature, 2023. p. 539-549 (Lecture Notes in Production Engineering; Vol. Part F1764).

Research output: Chapter in book/report/conference proceedingContribution to book/anthologyResearchpeer review

Lukas, M, Leineweber, S, Reitz, B, Overmeyer, L, Aschemann, A, Klie, B & Giese, U 2023, Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach. in Lecture Notes in Production Engineering. Lecture Notes in Production Engineering, vol. Part F1764, Springer Nature, pp. 539-549. https://doi.org/10.1007/978-3-031-47394-4_52
Lukas, M., Leineweber, S., Reitz, B., Overmeyer, L., Aschemann, A., Klie, B., & Giese, U. (2023). Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach. In Lecture Notes in Production Engineering (pp. 539-549). (Lecture Notes in Production Engineering; Vol. Part F1764). Springer Nature. https://doi.org/10.1007/978-3-031-47394-4_52
Lukas M, Leineweber S, Reitz B, Overmeyer L, Aschemann A, Klie B et al. Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines: An Artificial Neural Network-Based Approach. In Lecture Notes in Production Engineering. Springer Nature. 2023. p. 539-549. (Lecture Notes in Production Engineering). doi: 10.1007/978-3-031-47394-4_52
Lukas, M. ; Leineweber, S. ; Reitz, B. et al. / Data Mining-Enabled Temperature Control for Sustainable Production in Rubber Extrusion Lines : An Artificial Neural Network-Based Approach. Lecture Notes in Production Engineering. Springer Nature, 2023. pp. 539-549 (Lecture Notes in Production Engineering).
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