Speaker
Description
The main aim of this research is to study the mechanical behavior and damage evolution of additively manufactured spinodoid metamaterials under quasi-static loading. Additive manufacturing (AM) is a transformative method that constructs parts layer-by-layer from a digital 3D model, enabling the creation of complex geometries and reducing costs associated with assembly. Spinodoid metamaterials are a class of architected metamaterials derived from spinodal decomposition principles, are particularly well-suited for fabrication using AM techniques like laser powder bed fusion (LPBF). These structures possess tunable mechanical properties, such as direction-specific strength and smooth property gradation, making them highly versatile for a wide variety of engineering applications in aerospace, construction, and medical industries.
The design process involves generating spinodoid structures through Gaussian Random Fields (GRFs), which produce microstructures with spatially varying densities. These variations significantly influence the mechanical behaviour of the structure, especially under tensile and compressive loading. Damage initiation and progression play a pivotal role in determining how these materials deform. Understanding these processes is essential for optimizing their performance and ensuring reliable designs. To achieve accurate predictions of mechanical behavior, the present study incorporates both local and non-local damage models into the simulation framework. Non-local approaches, such as implicit gradient damage models, address issues like localization and enhance physical accuracy, which is very important to design these spinodoid’s for engineering applications.
By integrating advanced damage modeling techniques, the current study aims to establish a robust framework for simulating the performance of additive manufactured structures, while underscoring the immense potential of spinodoid metamaterials for applications requiring customized mechanical properties. The integration of damage modeling enables precise predictions of mechanical performance, supporting the robust design of next-generation metamaterials. The findings are particularly relevant for industries prioritizing lightweight structures, biomedical implants, and energy absorption systems. This research lays the foundation for broader adoption by demonstrating a robust framework for designing innovative and customizable materials.
