Predicting elastic modulus of a porous structure is essential for applications in aerospace, biomedical, and structural engineering. Traditional methods often struggle to capture complex relationships between material properties, design variables, and mechanical behavior. This study employed artificial neural networks (ANNs) to predict the elastic modulus of a porous structure based on various material and design parameters. An ANN model was trained on a dataset generated via finite element analysis (FEA) simulations, covering diverse combinations of material properties and design variables (e.g., porosity, structure types). The model demonstrated high accuracy in predicting the elastic modulus on a separate test dataset. Key findings included identification of significant design variables influencing the elastic modulus and the ANN model"s ability to generalize predictions to new data. This approach showcases that ANN is a powerful tool for designing and optimizing porous structures, providing reliable mechanical property predictions without extensive experimental testing or complex simulations. The proposed method can enhance design efficiency and pave the way for developing advanced materials with tailored mechanical properties. Future research will extend the model to predict other mechanical properties and incorporate experimental validation to verify ANN predictions.

In this paper, we present a simple and robust fabrication method for mushroom-shaped microstructures using diverse polymers with various modulus of elasticity. Through the repeated replica molding process, we fabricated the same PDMS mushroom structure negative mold as the prepared silicon master mold. To evaluate the fabricating stability of the fabricated PDMS negative mold, the mushroom-shaped structures were replicated from the mold using six types of polymer resins with different elastic modulus and we measured superhydrophobic properties on the samples. All the fabricated samples exhibited superhydrophobicity, and we proved the structural stability of the proposed replication method through the measured SEM images, contact angles on the samples, and theoretical analysis based on the structural shape.