Issue 66

A. Anjum et alii, Frattura ed Integrità Strutturale, 66 (2023) 112-126; DOI: 10.3221/IGF-ESIS.66.06

was used for the cracked plate and adhesive layer, a higher-order 20-noded element that is suited for solid structure analysis and recommended for linear application. To optimize run time and processor requirements, only one-quarter of the plate was considered due to symmetry, and a fine mesh was created around the fracture front to determine the SIF. The interaction integral was employed to compute the SIF, using the virtual crack extension principles technique, which produced accurate results with fewer mesh requirements. To create a singularity in stresses and strains around the crack tip, singular elements were used, ideally oriented in the same direction as the crack. Fig. 2 displays the complete set of FE models for all aspects of the quarter model generated based on the given boundary conditions. The crack front was modelled using ten singular elements, while the PZT actuator was modelled using 4,999 coupled-field elements. The damaged plate was modelled using 12,393 high-order reduced integration solid elements, and the adhesive bond was modelled using 2,499 high order reduced integration solid elements.

Figure 2. FE mesh model of the repaired plate.

Machine learning approach Machine learning (ML) is a type of artificial intelligence that involves the use of algorithms to automatically learn from data, identify patterns, and make decisions without explicit instructions. ML is a powerful tool for data analysis that can automate the development of analytical models. However, there is no one-size-fits-all approach to using machine learning to solve real-world problems, as different problems require different methods. Fig. 3 provides an overview of the general process of using machine learning to analyses data.

Figure 3. Machine Learning Process. In this study, we examined the impact of actuator position, cross-sectional area, thickness, and adhesive thickness on the SIF using finite element simulations. Our dataset includes 4 features and 1 target value. Tab. 2 presents the features X1, X2, X3, and X4, which correspond to actuator position, cross-sectional area, thickness, and adhesive thickness, respectively. The target value, Y1, represents the SIF and is the goal parameter of our machine learning approach. Tab. 2 provides an overview of these characteristics and the desired values. We collected 27 data points through numerical simulations for our dataset.

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