• Composites Research
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• v.36 no.1
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• pp.53-58
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• 2023

The PIC design method is assigning different stacking sequences for each shell element through the preliminary FE analysis. In previous study, machine learning was applied to the PIC design method in order to assign the region efficiently, and the training data is labeled by dividing each region into tension, compression, and shear through the preliminary FE analysis results value. However, since buckling is not considered, when buckling occurs, it can't be divided into appropriate loading type. In the present study, it was proposed PIC-NTL (PIC design using novel technique for analyzing load type) which is method for applying a novel technique for analyzing load type considering buckling to the conventional PIC design. The stress triaxiality for each ply were analyzed for buckling analysis, and the representative loading type was designated through the determined loading type within decision area divided into two regions of the same size in the thickness direction of the elements. The input value of the training data and label consisted in coordination of element and representative loading type of each decision area, respectively. A machine learning model was trained through the training data, and the hyperparameters that affect the performance of the machine learning model were tuned to optimal values through Bayesian algorithm. Among the tuned machine learning models, the SVM model showed the highest performance. Most effective stacking sequence were mapped into PIC tube based on trained SVM model. FE analysis results show the design method proposed in this study has superior external loading resistance and energy absorption compared to previous study.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.267-274
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• 2024

Hydrogen embrittlement fracture poses a challenge in ensuring the structural integrity of materials exposed to hydrogen-rich environments. This study advances our comprehension of hydrogen-induced fracture through an integrated numerical modeling approach. In addition, it employs a ductile fracture model named the Gurson-cohesive model (GCM) and hydrogen diffusion analysis. GCM is employed as a fracture model that combines the Gurson model to illustrate the continuum damage evolution and the cohesive zone model to describe crack surface discontinuity and softening behavior. Moreover, porosity and stress triaxiality are considered as crack initiation criteria . A hydrogen diffusion analysis is also integrated with the GCM to account for hydrogen enhanced decohesion (HEDE) mechanisms and their subsequent impacts on crack initiation and propagation. This framework considers the influence of hydrogen on the softening behavior of the traction-separation relationship on the discontinuous crack surface. Parametric studies explore the sensitivity to diffusion properties and hydrogen-induced fracture properties. By combining numerical models of hydrogen diffusion and the ductile fracture model, this study provides an understanding of hydrogen-induced fracture and thereby contributes significantly to the ongoing efforts to design materials that are resilient to hydrogen embrittlement in practical engineering applications.