Damage Initiation and Propagation in Hybrid Composite Structures

2018 ◽  
Author(s):  
Masoud Yekani Fard ◽  
Brian Raji ◽  
John Woodward ◽  
Mateo Oramas ◽  
Michael Padilla ◽  
...  
Keyword(s):  
Hybrid Composite ◽  
Composite Structures ◽  
Damage Initiation ◽  
Initiation And Propagation

scholarly journals Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2005
Author(s):  
Veronika Scholz ◽  
Peter Winkler ◽  
Andreas Hornig ◽  
Maik Gude ◽  
Angelos Filippatos
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Damage Identification ◽  
Vibration Response ◽  
Damage Initiation ◽  
Matrix Cracks ◽  
Operational Life ◽  
Multiple Data Sets ◽  
Fully Connected

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

Integrally-Stiffened Composite Damage Tolerance Evaluated by Computational Simulation

2001 ◽  
Author(s):  
Christos C. Chamis ◽  
Levon Minnetyan
Keyword(s):  
Material Properties ◽  
Composite Structures ◽  
Failure Modes ◽  
Damage Tolerance ◽  
Epoxy Composite ◽  
Computational Simulation ◽  
Computer Code ◽  
Simulation Design ◽  
Damage Initiation ◽  
Composite Damage

Abstract An integrally stiffened graphite/epoxy composite rotorcraft structure is evaluated via computational simulation. A computer code that scales up constituent micromechanics level material properties to the structure level and accounts for all possible failure modes is used for the simulation of composite degradation under loading. Damage initiation, growth, accumulation, and propagation to fracture are included in the simulation. Design implications with regard to defect and damage tolerance of integrally stiffened composite structures are examined. A procedure is outlined regarding the use of this type of information for setting quality acceptance criteria, design allowables, damage tolerance, and retirement-for-cause criteria.

Dynamic properties of hybrid composite structures based multiwalled carbon nanotubes

2017 ◽  
Vol 148 ◽  
pp. 70-79 ◽  
Author(s):  
H. Benyahia ◽  
M. Tarfaoui ◽  
V. Datsyuk ◽  
A. El Moumen ◽  
S. Trotsenko ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Multiwalled Carbon Nanotubes ◽  
Hybrid Composite ◽  
Composite Structures ◽  
Dynamic Properties ◽  
Multiwalled Carbon

scholarly journals Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Composite Structures ◽  
Structural Damage ◽  
Structural Integrity ◽  
State Of The Art ◽  
Future Trends ◽  
Learning Models ◽  
Damage Diagnosis ◽  
Comprehensive Review ◽  
Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

Numerical and digital image correlation analysis of tensile behavior of thin-ply hybrid laminates with discontinuous carbon sandwiched by continuous glass and carbon

2021 ◽  
pp. 002199832110565
Author(s):  
Amos Ichenihi ◽  
Wei Li ◽  
Li Zhe
Keyword(s):  
Digital Image Correlation ◽  
Digital Image ◽  
Element Model ◽  
Image Correlation ◽  
Continuous Glass ◽  
Damage Initiation ◽  
3D Digital Image Correlation ◽  
Initiation And Propagation ◽  
Hybrid Laminates ◽  
Strain Increase

Thin-ply hybrid laminates of glass and carbon fibers have been widely adopted in engineering pseudo-ductility. In this study, a Finite Element model is proposed using Abaqus to predict pseudo-ductility in thin-ply laminates consisting of three materials. These materials comprise continuous carbon (CC) and continuous glass sandwiching partial discontinuous carbon (DC). The model adopts the Hashin criterion for damage initiation in the fibers and the mixed-mode Benzeggagh-Kenane criterion on cohesive surfaces for delamination initiation and propagation. Numerically predicted stress–strain results are verified with experimental results under tensile loading. Results show pseudo-ductility increases with the increase in DC layers, and pseudo-yield strength and strain increase with the increase in CC layers. 3D-Digital Image Correlation results indicate delamination growth on pseudo-ductile laminates, and the calculated Poisson’s ratios show pseudo-ductility occurs below 0.27. Moreover, Poisson’s ratio decreases with an increase in pseudo-ductility.

RESIDUAL PLASTIC STRAIN STATE AND DELAMINATION PATTERN OF COMPOSITE LAMINATES WITH AUTOMATED INDUCED-GAP SUBJECTED TO HEMI-SPHERICAL IMPACT LOADING

2021 ◽  
Author(s):  
MOHAMMADHOSSEIN GHAYOUR ◽  
MEHDI HOJJATI ◽  
RAJAMOHAN GANESAN
Keyword(s):  
Plastic Strain ◽  
Impact Loading ◽  
Residual Strain ◽  
Composite Structures ◽  
Strain State ◽  
Composite Plates ◽  
Structural Level ◽  
Manufacturing Defects ◽  
Initiation And Propagation ◽  
The Impact

Automated manufacturing defects are types of composite structure defects that occur during fiber deposition by advanced robots. The induced gap is the most probable type of defect in the Automated Fiber Placement (AFP) technique. This defect can affect the mechanical performance of the composite structures at both material level by inducing the material inhomogeneity and the structural level by introducing the consolidation effect in the structure during the curing process. The current study investigates the effect of induced-gaps on the damage assessment of thin composite plates under Low-Velocity Impact (LVI) loading. The paper focuses on the delamination initiation and propagation and the residual plastic strain state of the impacted plates. The primary application of this study is to understand the interaction of induced gaps on the delamination pattern of composite samples subjected to LVI. For this purpose, a series of LVI tests are performed. Ultrasonic C-scan analysis and microscopic observation are implied to evaluate the internal damage due to impact loading. Finite Element (FE) analyses are then performed to evaluate the residual strain of the composite plates under Impact Energy (IE) loading less than 15 J. Then, the residual plastic strain in the impact zone is evaluated using a meso-macro method, and the effect of the local plasticity that occurs in the gap zones on the delamination initiation and propagation is studied. Results show that the stress relaxation due to the resin plasticity at the gap areas can affect the delamination pattern of the impacted composite plates. It is also shown that the residual strain of the impacted plates at the gap areas are new sources of the damages that need to be considered in the LVI analysis of the composite plates manufactured by the AFP technique.

PROGRESSIVE DAMAGE FAILURE ANALYSIS OF POST-BUCKLED COMPOSITE SINGLE-STRINGER PANEL WITH TEFLON INSERTS

2021 ◽  
Author(s):  
VIJAY K. GOYAL ◽  
AUSTIN PENNINGTON ◽  
JASON ACTION
Keyword(s):  
Experimental Data ◽  
Composite Structures ◽  
Laminated Composites ◽  
Compressive Loading ◽  
Weight Ratio ◽  
High Strength ◽  
Material Model ◽  
Stiffened Panels ◽  
Damage Initiation ◽  
Critical Aspects

The high strength-to-weight and stiffness-to-weight ratio materials, such as laminated composites, are advantageous for modern aircraft. Laminated composites with initial flaws are susceptible to delamination under buckling loads. PDA tools help enhance the industry’s understanding of the mechanisms for damage initiation and growth in composite structures while assisting in the design, analysis, and sustainment methods of these composite structures. The global-local modeling approach for the single-stringer post-buckled panel was evaluated through this effort, using Teflon inserts to simulate the defect of damage during manufacturing. This understanding is essential for designing the post-buckled structure, reducing weight while predicting damage initiation location, and addressing a potential design review for future aircraft repairs. In this work, the initial damage was captured with Teflon inserts as the starting configuration; and any reference to the damage initiation refers to any damage beyond the “initial unbonded region.” The effort aims to develop, evaluate, and enhance methods to predict damage initiation and progression and the failure of post-buckled hat-stiffened panels using multiple Abaqus FEA Virtual Crack Closure Technique (VCCT) definitions. Validation of the PDA using the VCCT material model was performed on a large single-stringer panel subjected to compressive loading. The compressive loading of the panel caused the skin to buckle before any damage began to occur locally. In addition, comparisons are made for critical aspects of the damage morphology, such as a growth pattern that included delamination from the skin-stiffener interface to the skin and ply interfaces. When compared against the experimental data produced through the NASA Advanced Composites Project (ACP), the present model captured damage migration from one surface to another, and model validations were ~5% of the experimental data.

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