Multiscale Modeling of Crack Formation in Composite Laminates with Manufacturing Defects

2018 ◽  
Author(s):  
RAMESH TALREJA
Keyword(s):  
Multiscale Modeling ◽  
Composite Laminates ◽  
Crack Formation ◽  
Manufacturing Defects

Multiscale modeling of free-surface effect on crack formation in unidirectional off-axis laminates

2017 ◽  
Vol 98 ◽  
pp. 136-146 ◽  
Author(s):  
Yuta Kumagai ◽  
Sota Onodera ◽  
Yoshiko Nagumo ◽  
Tomonaga Okabe ◽  
Kenichi Yoshioka
Keyword(s):  
Free Surface ◽  
Multiscale Modeling ◽  
Crack Formation ◽  
Surface Effect

Predicting Formation of Manufacturing Defects in Contoured Composites

2019 ◽  
Vol 64 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Guillaume Seon ◽  
Yuri Nikishkov ◽  
Andrew Makeev ◽  
Lauren Fergusson
Keyword(s):  
Finite Element ◽  
Manufacturing Process ◽  
Composite Laminates ◽  
Defect Formation ◽  
Radius Of Curvature ◽  
Void Content ◽  
Consolidation Process ◽  
Closed Cavity ◽  
Manufacturing Defects ◽  
Early Stages

Composite helicopter rotor components are typically thick and often have areas with a tight radius of curvature, which make them especially prone to process-induced defects, including wrinkles and voids at ply interfaces. Such flaws cause high rejection rates in production of flight-critical components and structures. This work seeks to fill the gaps in understanding generation of the noted defects in contoured polymer–matrix composite laminates. In particular, understanding and modeling defect formation at the early stages of the manufacturing process might be the missing link to enable the development of practical engineering solutions allowing for better control of the manufacturing process of contoured composite parts. In this work, an approach based on a continuum description of the uncured prepreg material, including the initial bulk or void content, and finite element modeling (FEM) is used to simulate the consolidation process at the early stages of manufacturing of contoured laminates. The simulation predicts instabilities leading to formation of both wrinkles and voids at ply interfaces during laminate debulking or vacuum consolidation. Applicability of the method to consolidation in both closed-cavity and open-face tooling is also demonstrated. FEM results show good correlation with X-ray computed tomography data. This work also introduces a new simulation concept based on finite element and discrete modeling of voids at ply interfaces to improve the accuracy of predicting their evolution during the debulking operations.

Induced defect layer method to characterize the effect of fiber tow gaps for the laminates manufactured by automated fiber placement technique

2021 ◽  
pp. 002199832110316
Author(s):  
Mohammadhossein Ghayour ◽  
Mehdi Hojjati ◽  
Rajamohan Ganesan
Keyword(s):  
Composite Laminates ◽  
Composite Structures ◽  
Structural Integrity ◽  
Mechanical Response ◽  
Defect Layer ◽  
Damage Analysis ◽  
Manufacturing Defects ◽  
Fiber Placement ◽  
Layer Method ◽  
Automated Fiber Placement

Automated manufacturing defects are new types of composite structure defects induced during fiber deposition by robots. Fiber tow gap is one of the most probable types of defects observed in the Automated Fiber Placement (AFP) technique. This defect can affect the structural integrity of structures by reducing structural strength and stiffness. The effect of this defect on the mechanical response of the composite laminates has been investigated experimentally in the literature. However, there is still no efficient numerical/analytical method for damage assessment of composite structures with distributed induced gaps manufactured by the AFP technique. The present paper aims to develop the Induced Defect Layer Method (IDLM), a new robust meso-macro model for damage analysis of the composite laminates with gaps. In this method, a geometrical parameter, Gap Percentage (GP), is implemented to incorporate the effect of induced-gaps in the elastic, inelastic, and softening behavior at the material points. Thus, while the plasticity and failure of the resin pockets in conjunction with intralaminar composite damages can be evaluated by this method, the defective areas are not required to be defined as resin elements in the Finite Element (FE) models. It can also be applied for any arbitrary distributions of the defects in the multi-layer composite structures, making it a powerful tool for continuum damage analysis of large composite structures. Results indicate that the proposed method can consider the effect of gaps in both elastic and inelastic behavior of the composite laminate with defects. It also provides good agreement with the experimental results.

scholarly journals A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2335 ◽  
Author(s):  
Asif Khan ◽  
Jae Kyoung Shin ◽  
Woo Cheol Lim ◽  
Na Yeon Kim ◽  
Heung Soo Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Composite Laminates ◽  
Laminated Composite ◽  
Test Accuracy ◽  
Smart Composite ◽  
Manufacturing Defects ◽  
Learning Framework ◽  
Unseen Data

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

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