Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design

Khazar Hayat; Zahur Ullah; Shafaqat Siddique; Zeshan Ahmad

doi:10.1051/matecconf/202338101011

Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design

Khazar Hayat, Zahur Ullah, Shafaqat Siddique, Zeshan Ahmad

School of Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elastic properties of woven fabric composites, which can be estimated through analytical, numerical, or experimental means. In this study, we propose a novel approach that combines machine learning techniques with finite-element methods based multi-scaling analysis methodology to predict the elastic behaviour of woven composites. The method leverages datasets generated from finite element methods based numerical simulations and literature to train and validate models, providing a cost-effective and computationally efficient alternative to conventional homogenization-based finite element method. The approach offers a promising solution to accurately predicting the elastic behaviour of woven fabric composites.

Original language	English
Title of host publication	Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023
Publisher	EDP Sciences
Number of pages	9
DOIs	https://doi.org/10.1051/matecconf/202338101011
Publication status	Published - 13 Jun 2023
Event	1st International Conference on Modern Technologies in Mechanical & Materials Engineering 2023 - Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan Duration: 06 May 2023 → 06 May 2023 https://giki.edu.pk/mtme2023/

Publication series

Name	MATEC Web of Conferences
Volume	381
ISSN (Electronic)	2261-236X

Conference

Conference	1st International Conference on Modern Technologies in Mechanical & Materials Engineering 2023
Abbreviated title	MTME-2023
Country/Territory	Pakistan
City	Topi
Period	06/05/2023 → 06/05/2023
Internet address	https://giki.edu.pk/mtme2023/

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10.1051/matecconf/202338101011Licence: CC BY

Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design
Copyright 2023 The Authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 856 KBLicence: CC BY

Cite this

Hayat, K., Ullah, Z., Siddique, S., & Ahmad, Z. (2023). Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design. In Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023 Article 01011 (MATEC Web of Conferences; Vol. 381). EDP Sciences. https://doi.org/10.1051/matecconf/202338101011

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title = "Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design",

abstract = "Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elastic properties of woven fabric composites, which can be estimated through analytical, numerical, or experimental means. In this study, we propose a novel approach that combines machine learning techniques with finite-element methods based multi-scaling analysis methodology to predict the elastic behaviour of woven composites. The method leverages datasets generated from finite element methods based numerical simulations and literature to train and validate models, providing a cost-effective and computationally efficient alternative to conventional homogenization-based finite element method. The approach offers a promising solution to accurately predicting the elastic behaviour of woven fabric composites.",

author = "Khazar Hayat and Zahur Ullah and Shafaqat Siddique and Zeshan Ahmad",

year = "2023",

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language = "English",

series = "MATEC Web of Conferences",

publisher = "EDP Sciences",

booktitle = "Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023",

note = "1st International Conference on Modern Technologies in Mechanical & Materials Engineering 2023, MTME-2023 ; Conference date: 06-05-2023 Through 06-05-2023",

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Hayat, K, Ullah, Z, Siddique, S & Ahmad, Z 2023, Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design. in Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023., 01011, MATEC Web of Conferences, vol. 381, EDP Sciences, 1st International Conference on Modern Technologies in Mechanical & Materials Engineering 2023, Topi, Pakistan, 06/05/2023. https://doi.org/10.1051/matecconf/202338101011

Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design. / Hayat, Khazar; Ullah, Zahur; Siddique, Shafaqat et al.
Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023. EDP Sciences, 2023. 01011 (MATEC Web of Conferences; Vol. 381).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design

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AU - Siddique, Shafaqat

AU - Ahmad, Zeshan

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N2 - Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elastic properties of woven fabric composites, which can be estimated through analytical, numerical, or experimental means. In this study, we propose a novel approach that combines machine learning techniques with finite-element methods based multi-scaling analysis methodology to predict the elastic behaviour of woven composites. The method leverages datasets generated from finite element methods based numerical simulations and literature to train and validate models, providing a cost-effective and computationally efficient alternative to conventional homogenization-based finite element method. The approach offers a promising solution to accurately predicting the elastic behaviour of woven fabric composites.

AB - Woven fabric reinforced plastic composites are highly favoured in the aerospace and automotive industries for their exceptional impact resistance and ease of manufacture. To design and analyse these structures, it is crucial to determine their elastic properties of woven fabric composites, which can be estimated through analytical, numerical, or experimental means. In this study, we propose a novel approach that combines machine learning techniques with finite-element methods based multi-scaling analysis methodology to predict the elastic behaviour of woven composites. The method leverages datasets generated from finite element methods based numerical simulations and literature to train and validate models, providing a cost-effective and computationally efficient alternative to conventional homogenization-based finite element method. The approach offers a promising solution to accurately predicting the elastic behaviour of woven fabric composites.

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DO - 10.1051/matecconf/202338101011

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Hayat K, Ullah Z, Siddique S, Ahmad Z. Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design. In Proceedings of the 1st International Conference on Modern Technologies in Mechanical & Materials Engineering, MTME-2023. EDP Sciences. 2023. 01011. (MATEC Web of Conferences). doi: 10.1051/matecconf/202338101011

Exploring the elastic properties of woven fabric composites: a machine learning approach for improved analysis and design

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