Minimizing Material Usage While Preserving Strength: Hybrid Genetic and Topology Optimization Approaches in Additive Manufacturing

Abstract

Additive manufacturing technology has enabled the fabrication of intricate geometric constructs utilizing novel methodologies. Nonetheless, the optimization of material utilization while concurrently preserving structural integrity remains a pivotal technical endeavor. This investigation delves into the mathematical principles underlying topology optimization methodologies and their amalgamation with biomimetic lattice configurations. A comprehensive examination of four principal topology optimization methodologies—SIMP, BESO, Level Set, and ESO—is provided. An evaluative comparison of the advantages, disadvantages, and applications of each methodology is conducted. While SIMP demonstrates superiority in computational efficiency, BESO enhances the clarity of material boundaries. Level Set is useful for shapes that are hard to picture, while ESO is useful in the early stages of the design process. The study delves deeper into the traits of lattice structures inspired by natural forms and examines approaches to enhance their functional capabilities. Combining evolutionary algorithms with topology optimization is a good idea since it lets you search the entire design space while also making small improvements at the same time. The current literature indicates that the hybrid SIMP-GA methodology has attained roughly 7% enhanced compliance levels in comparison to conventional gradient-based strategies. This theoretical investigation integrates mathematical methodologies that optimize the efficiency of additive manufacturing while safeguarding structural integrity.