Delamination and thrust force analysis in GLARE: Influence of tool geometry and prediction with machine learning models
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sage Publications Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The multi-layered (fiber/metal) structure of glass fibre aluminium reinforced epoxy (GLARE) makes it difficult to obtain acceptable damage-free holes that meet aerospace standards. This paper investigated the effects of tool geometry and drilling parameters on reducing delamination damage and uncut fibers at the hole exit surface in drilling GLARE. The hole surfaces were examined by scanning electron microscope (SEM) at various magnifications. In addition, deep neural network (DNN) and long-short-term memory (LSTM) machine learning models were used to predict delamination (Fda), uncut fiber (UCF), and thrust forces using experimental data. No positive contribution of the special geometry tool was observed, while the standard geometry tool was found to be ideal for drilling conditions. Analysis of variance (ANOVA) revealed that feed rate contributed 57.83% to delamination damage, while tool geometry contributed 74.31% and 92.33% for uncut fiber and thrust force, respectively. SEM analysis revealed high deformation zones in the aluminum layers and fiber fracture and separation in the glass fibre reinforced polymer (GFRP) layers. DNN and LSTM models were found to provide accurate predictions with R2 values greater than 95% and 98%, respectively.
Açıklama
Anahtar Kelimeler
Glass fibre aluminium reinforced epoxy, delamination, uncut fiber, analysis of variance, machine learning, long-short-term memory
Kaynak
Journal of Composite Materials
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
