Estimation of Impact Energies in Composites Using an Out-of-Distribution Generalization of Stacked Models Trained with Shearography and Thermography Images

• Published in: Journal of nondestructive evaluation 2021-09, Vol.40 (3), Article 72
• Main Authors: Fröhlich, Herberth Birck, de Oliveira, Bernardo Cassimiro Fonseca, Gonçalves, Armando Albertazzi
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Characterization and Evaluation of Materials; Classical Mechanics; Composite materials; Composite structures; Control; Dynamical Systems; Energy value; Engineering; Impact tests; Nondestructive testing; Regression models; Shearography; Solid Mechanics; Thermography; Vibration
• ISSN: 0195-9298; 1573-4862
• DOI: 10.1007/s10921-021-00809-2

Shearography and thermography are two well-established nondestructive testing methods. Yet, both methods present high subjectivity in the interpretation of their results, which difficulties the automation. Many efforts go towards applying intelligent algorithms, drawing together many limitations, such as the need for a large data set and fine-tuning. In addition, the classification task to categorize defects is often used to check continuous parameters, such as impact energies, leading to the wrong interpretation of the results. In this work, we train stacked models for impact energy estimation in composites with images from both shearography and thermography. The base estimators feeding these stacked models are random forests trained with the standard deviation curves of the defect images and residual neural networks trained with the test images. Such images come from controlled impact tests performed on composite plates. The ability to estimate continuous impact energy values is also analyzed, as well as the capacity of these networks to make an out-of-distribution generalization, which provides error results in the order of 0.2 J mean absolute error for energies values never seen in the training progress. Choosing such regression models trained with the combination of two non-destructive tests enables the reduction of the dataset size and the monitoring of the defect progression by analyzing continuous energy values.