Deep learning-enabled design for tailored mechanical properties of SLM-manufactured metallic lattice structures

Abstract

The lattice structures obtained by combining repetitive light cellular forms provide superior high strength to weight capabilities compared to monolithic solid bodies. These properties of lattice structures can be further enhanced by improving the design and manufacturing process. However, the process of creating aluminum alloy lattice structures with tailored mechanical properties is still challenging due to the wide range of possible designs and their complex structure-property relationships. This paper proposes a new methodology that uses the deep learning-based 3D Generative Adversarial Network (3DGAN) model to solve this complex engineering problem. Unlike previous studies on deep learning-based lattice design, the dataset obtained through parametric design and Simulated Annealing techniques enables GAN to create new 3D lattice structures with improved mechanical strength properties. The designs generated with the GAN algorithm were produced using Selective Laser Melting Additive Manufacturing (SLM-AM) technology. The mechanical properties of SLM-fabricated (SLMed) AlSi10Mg unit cell samples were examined by a series of compression and impact tests. The results reveal that the lattice configurations generated using the generative model exhibit improved mechanical properties (e.g., a remarkable increase in normalized energy absorption and extension capacities of up to 57% and 26%, respectively). This study shows that designs generated with the 3DGAN model in lattice structures improve the design space by improving their mechanical properties. Moreover, the successful integration of deep learning and SLM-AM opens up new possibilities for creating custom parts with improved strength-to-weight ratios, dimensional accuracy, and mechanical performance.