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

被引:6
作者
Osa-uwagboe, Norman [1 ,2 ]
Udu, Amadi Gabriel [2 ,3 ]
Silberschmidt, Vadim V. [1 ]
Baxevanakis, Konstantinos P. [1 ]
Demirci, Emrah [1 ]
机构
[1] Loughborough Univ, Wolfson Sch Mech Elect & Mfg Engn, Loughborough LE11 3TU, England
[2] Nigerian Air Force Base, Air Force Res & Dev Ctr, PMB 2104, Kaduna, Nigeria
[3] Univ Leicester, Sch Engn, Leicester LE1 7RH, England
关键词
composite sandwich; machine learning; acoustic emission; damage prediction; seawater exposure;
D O I
10.3390/ma17112549
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
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.
引用
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页数:25
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