A novel design framework for generating functionally graded multi-morphology lattices via hybrid optimization and blending methods

Official URL: https://dx.doi.org/10.1016/j.addma.2023.103560

Owing to its excellent mechanical properties, triply periodic minimum surfaces (TPMS) lattice structures have recently gained more interest in engineering applications. The superior properties of these structures make it easier to achieve engineering design goals such as strength and weight. However, technological advancements compel the designer to enhance the traditional TPMS design qualities. Hybridization of different lattice types emerges as a strong candidate for enhancing overall design performance. Therefore, a hybrid optimization scheme based on genetic algorithms (GA) and anisotropic homogenization-based topology optimization is considered to generate a functionally graded multi-morphology for a Messerschmitt–Bölkow–Blohm (MBB) beam design in this paper. The GA is performed to identify the best lattice morphology, including Diamond (D), Gyroid (G), I-WP, and Primitive (P), and their relative densities prior to topology optimization (TO). Once the best lattice morphology of the design domain is obtained via the GA, the homogenization-based topology optimization is applied to grade the multi-morphology lattice to improve the design performance further. The final step is the reconstruction of the graded multi-morphology using a novel blending algorithm. The reconstructed MBB beams are made of cobalt-chromium (CoCr) alloy and are then manufactured using the laser sintering method, direct metal laser melting (DMLM) technique. Destructive metallographic and non-destructive metrological techniques are utilized to assure manufacturing quality. An impact hammer test is conducted on the fabricated beams to validate and compare the proposed graded multi-morphology geometry with graded and uniform single lattice morphologies. Experimental results show that the stiffness of the graded multi-morphology structure designed by the proposed hybrid optimization is 4.5 % and 13.0 % higher than the graded form of D and P-type single lattice morphologies, respectively. Also, it is observed that the graded form single lattice morphologies deliver superior performance than their uniform encounters namely D and P-type lattice structures.