Towards a multi-fidelity deep learning framework for a fast and realistic generation of ultrasonic multi-modal Total Focusing Method images in complex geometries

Govanni, Granados,; Miorelli, Roberto; Gatti, Filippo; Robert, Sébastien; Clouteau, Didier

doi:10.1016/j.ndteint.2023.102906

Cited by 9 publications

(1 citation statement)

References 39 publications

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“…For instance, methodologies have been proposed to reconstruct intricate material maps and decode crystallographic orientations using ultrasonic data [44,45]. Further enhancements in the domain introduced tailored deep learning models, such as the Conditional U-Net, to rapidly generate ultrasonic images and bridging physics [46]. Moreover, the integration of specialized geometric regularization techniques, like non-uniform rational B-splines with convolutional autoencoders, showcases the versatility of such architectures in predicting scattered geometries [47,48].…”

Section: Introductionmentioning

confidence: 99%

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

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This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.

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