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.

Micro cutting of titanium alloy by polycrystalline diamond (PCD) tools was studied. Micro electro discharge machining (MEDM) was used to fabricate customized micro shaping tools from PCD blank. The tool was used to machine micro grooves on Ti alloy and the effects of depth of cut and machining length on tool wear, burr and surface roughness were studied. The shaping tool has cutting edge of a few μm. The crater size of the tool surface was increased with increasing capacitance of EDM machining conditions, which was used to control the surface roughness of the machined micro grooves.