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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Soudagar, Manzoore Elahi M.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Optimizing Friction Stir Processing Parameters for Aluminium Alloy 2024 Reinforced with SiC Particles: A Taguchi Approach of Investigation
- 2024Leverage of aluminium oxynitride on the impact resistance of Kevlar‐impregnated epoxy composites: Experimental and numerical evaluation under low‐velocity impactcitations
- 2024Physiochemical and electrical activities of nano copper oxides synthesised <i>via</i> hydrothermal method utilising natural reduction agents for solar cell applicationcitations
- 2024Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters
- 2024Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters
- 2023Influence of Layering Pattern, Fibre Architecture, and Alkalization on Physical, Mechanical, and Morphological Behaviour of Banana Fibre Epoxy Compositescitations
- 2023Influence of Layering Pattern, Fibre Architecture, and Alkalization on Physical, Mechanical, and Morphological Behaviour of Banana Fibre Epoxy Compositescitations
- 2023Study on Interfacial Interaction of Cement-Based Nanocomposite by Molecular Dynamic Analysis and an RVE Approachcitations
- 2023Analytical modeling and experimental estimation of the dynamic mechanical characteristics of green composite: <i>Caesalpinia decapetala</i> seed reinforcementcitations
- 2023Effect of Caesalpinia decapetala on the Dry Sliding Wear Behavior of Epoxy Compositescitations
- 2022Investigation of Various Coating Resins for Optimal Anticorrosion and Mechanical Properties of Mild Steel Surface in NaCl Solutioncitations
- 2022Investigation of Various Coating Resins for Optimal Anticorrosion and Mechanical Properties of Mild Steel Surface in NaCl Solutioncitations
- 2022Effects of tin particles addition on structural and mechanical properties of eutectic Sn–58Bi solder jointcitations
- 2022Diesel Spray: Development of Spray in Diesel Enginecitations
- 2021Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Platescitations
- 2020Biodegradable carboxymethyl cellulose based material for sustainable packaging applicationcitations
Places of action
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article
Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates
Abstract
The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.