Product development based on metal 3D printing has been expanding steadily in the manufacturing industry. Powder bed fusion (PBF) is currently the most widely used metal 3D printing method, as it produces parts of complex shape with high precision. Applying PBF to the cutting tool industry could shorten process time relative to conventional sintering routes and reduce the material waste generated during machining. It also allows internal lattice structures and fluid channels to be designed within the tool, facilitating light-weighting and improved cutting performance. In this study, nTop software was first used to design the internal lattice structure of an end mill tool for additive manufacturing and to perform the corresponding simulation analysis. Three configurations were compared, in which the tool lattice was defined either by thickness dimension or by thickness range, and the reinforced results confirmed that none of the designs posed problems for actual additive manufacturing within the software. On this basis, tool bodies were fabricated on PBF metal 3D printing equipment, and their feasibility for tool applications was evaluated.

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.