Sensor for the Prognostics and Health Management of Multiple Impinging Jet Nozzles

Kang, Jong Hoon; Jung, Sung Yong

doi:10.1007/s40684-021-00414-8

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…The application of ML in composite materials extends across various stages in engineering analysis and design, for instance, encompassing design, 8 inspection, and prediction of failures. [9][10][11] For example, in the works by Gu and Chen, [12][13][14] the optimization of an interwoven material is studied, starting from the discretization of the system into equal regions, and addressing them as pixels of an image, each with three possible values or states for the modulus of elasticity. They then use convolutional neural networks to create metamodels capable of predicting mechanical properties such as strength and stiffness.…”

Section: Introductionmentioning

confidence: 99%

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

Ledesma-Orozco,

Galvis-Chacón,

Rodríguez-Sánchez

2024

Journal of Composite Materials

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In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.

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