DIGITALISED NONWOVEN MANUFACTURING FOR REDUCED ENERGY CONSUMPTION AND EFFICIENT PRODUCTION RATES

M. Murat Peksen; Cevza Candan; Banu Nergis; Bilge Koyuncu

doi:10.7216/teksmuh.teksmuh.1365862

DIGITALISED NONWOVEN MANUFACTURING FOR REDUCED ENERGY CONSUMPTION AND EFFICIENT PRODUCTION RATES

M. Murat Peksen
, Cevza Candan^*
, Banu Nergis
, Bilge Koyuncu

^*Corresponding author for this work

Department of Textile Engineering

Technical University of Munich
DeepTech Engineering Ltd.

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The requirements placed on the textile industry are changing drastically. Zero-emission targets for 2030 and beyond require cross-sectoral changes. This also challenges the textile industry to find viable all-in solutions that satisfy social, economic, and sustainable requirements. Currently, the use of textiles in transdisciplinary sectors demands a high level of tailor-made solutions. This requires a broad understanding of the processed materials, complex multiphysics as well as machine settings. Hence, to consolidate high production rates with optimal energy consumption and the use of sustainable resources, systematic research and product development need to be pursued. The digitalisation of research and production in the textile industry aids to close the gap between the complex production processes and the quality measure of the end products. For this purpose, a systematic approach comprising experimental measurements and emerging digital technologies is used. nonwoven manufacturing machine park results have been used for the benchmark study.

Translated title of the contribution	AZALTILMIŞ ENERJİ TÜKETİMİ VE VERİMLİ ÜRETİM ORANLARI İÇİN DİJİTALLEŞEN NONWOVEN ÜRETİMİ
Original language	English
Pages (from-to)	226-230
Number of pages	5
Journal	Tekstil ve Muhendis
Volume	30
Issue number	131
DOIs	https://doi.org/10.7216/teksmuh.teksmuh.1365862
Publication status	Published - 2023

Bibliographical note

Publisher Copyright:
© (2023). All Rights Reserved.

Funding

The authors gratefully acknowledge the R&D team of Low& Bonar (former COLBOND bv) for their technical support in experimental measurements that have been used as data in the research.

Funders
R&D team of Low& Bonar

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action

Keywords

AI
Digital twin
Digitalisation
Machine-Learning
Multiphysics
Nonwoven

Access to Document

10.7216/teksmuh.teksmuh.1365862

Cite this

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title = "DIGITALISED NONWOVEN MANUFACTURING FOR REDUCED ENERGY CONSUMPTION AND EFFICIENT PRODUCTION RATES",

abstract = "The requirements placed on the textile industry are changing drastically. Zero-emission targets for 2030 and beyond require cross-sectoral changes. This also challenges the textile industry to find viable all-in solutions that satisfy social, economic, and sustainable requirements. Currently, the use of textiles in transdisciplinary sectors demands a high level of tailor-made solutions. This requires a broad understanding of the processed materials, complex multiphysics as well as machine settings. Hence, to consolidate high production rates with optimal energy consumption and the use of sustainable resources, systematic research and product development need to be pursued. The digitalisation of research and production in the textile industry aids to close the gap between the complex production processes and the quality measure of the end products. For this purpose, a systematic approach comprising experimental measurements and emerging digital technologies is used. nonwoven manufacturing machine park results have been used for the benchmark study.",

keywords = "AI, Digital twin, Digitalisation, Machine-Learning, Multiphysics, Nonwoven",

author = "Peksen, \{M. Murat\} and Cevza Candan and Banu Nergis and Bilge Koyuncu",

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AU - Peksen, M. Murat

AU - Candan, Cevza

AU - Nergis, Banu

AU - Koyuncu, Bilge

PY - 2023

Y1 - 2023

N2 - The requirements placed on the textile industry are changing drastically. Zero-emission targets for 2030 and beyond require cross-sectoral changes. This also challenges the textile industry to find viable all-in solutions that satisfy social, economic, and sustainable requirements. Currently, the use of textiles in transdisciplinary sectors demands a high level of tailor-made solutions. This requires a broad understanding of the processed materials, complex multiphysics as well as machine settings. Hence, to consolidate high production rates with optimal energy consumption and the use of sustainable resources, systematic research and product development need to be pursued. The digitalisation of research and production in the textile industry aids to close the gap between the complex production processes and the quality measure of the end products. For this purpose, a systematic approach comprising experimental measurements and emerging digital technologies is used. nonwoven manufacturing machine park results have been used for the benchmark study.

AB - The requirements placed on the textile industry are changing drastically. Zero-emission targets for 2030 and beyond require cross-sectoral changes. This also challenges the textile industry to find viable all-in solutions that satisfy social, economic, and sustainable requirements. Currently, the use of textiles in transdisciplinary sectors demands a high level of tailor-made solutions. This requires a broad understanding of the processed materials, complex multiphysics as well as machine settings. Hence, to consolidate high production rates with optimal energy consumption and the use of sustainable resources, systematic research and product development need to be pursued. The digitalisation of research and production in the textile industry aids to close the gap between the complex production processes and the quality measure of the end products. For this purpose, a systematic approach comprising experimental measurements and emerging digital technologies is used. nonwoven manufacturing machine park results have been used for the benchmark study.

KW - AI

KW - Digital twin

KW - Digitalisation

KW - Machine-Learning

KW - Multiphysics

KW - Nonwoven

UR - https://www.scopus.com/pages/publications/85174688242

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DO - 10.7216/teksmuh.teksmuh.1365862

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ER -

DIGITALISED NONWOVEN MANUFACTURING FOR REDUCED ENERGY CONSUMPTION AND EFFICIENT PRODUCTION RATES

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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Cite this