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Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework

التفاصيل البيبلوغرافية
العنوان: Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework
المؤلفون: Norman Osa-uwagboe, Amadi Gabriel Udu, Vadim V. Silberschmidt, Konstantinos P. Baxevanakis, Emrah Demirci
المصدر: Materials, Vol 17, Iss 11, p 2549 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Electrical engineering. Electronics. Nuclear engineering
LCC:Engineering (General). Civil engineering (General)
LCC:Microscopy
LCC:Descriptive and experimental mechanics
مصطلحات موضوعية: composite sandwich, machine learning, acoustic emission, damage prediction, seawater exposure, Technology, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Engineering (General). Civil engineering (General), TA1-2040, Microscopy, QH201-278.5, Descriptive and experimental mechanics, QC120-168.85
الوصف: Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding their mechanical performance after moisture uptake and the implications of moisture uptake for their structural integrity and safety within out-of-plane loading regimes is vital for material optimisation. The use of modern methods such as acoustic emission (AE) and machine learning (ML) could provide effective techniques for the assessment of mechanical behaviour and structural health monitoring. In this study, the AE features obtained from quasi-static indentation tests on sandwich structures made from E-glass fibre face sheets with polyvinyl chloride foam cores were employed. Time- and frequency-domain features were then used to capture the relevant information and patterns within the AE data. A k-means++ algorithm was utilized for clustering analysis, providing insights into the principal damage modes of the studied structures. Three ensemble learning algorithms were employed to develop a damage-prediction model for samples exposed and unexposed to seawater and were loaded with indenters of different geometries. The developed models effectively identified all damage modes for the various indenter geometries under different loading conditions with accuracy scores between 86.4 and 95.9%. This illustrates the significant potential of ML for the prediction of damage evolution in composite structures for marine applications.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1996-1944
Relation: https://www.mdpi.com/1996-1944/17/11/2549; https://doaj.org/toc/1996-1944
DOI: 10.3390/ma17112549
URL الوصول: https://doaj.org/article/6d1688df60de4d84af9508687cc0591d
رقم الأكسشن: edsdoj.6d1688df60de4d84af9508687cc0591d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19961944
DOI:10.3390/ma17112549