ABSTRACT The vibrational behaviour of lightweight structural components, such as curved beams, is acutely sensitive to changes in geometric parameters, making the optimization of modal characteristics a crucial goal in vibration‐sensitive applications. While the specific effects of geometry on natural frequencies have been examined, a comprehensive synthesis of experimental and simulation data for optimal design identification remains limited. This study addresses the gap by adopting a hybrid methodology that integrates experimental modal analysis and finite element simulations with a Genetic Algorithm (GA)–driven optimization technique. A total of 27 design combinations, generated using a Taguchi Orthogonal design (L27) framework, were evaluated experimentally through an 8‐channel Dewesoft FFT analyzer, impact hammer, and dual‐accelerometer arrangement, as well as numerically. Curved beams with Triply Periodic Minimal Surface (TPMS) gyroid lattice infills were fabricated using Fused Deposition Modeling (FDM), and the first six vibrational modes were extracted to establish a surrogate model linking beam width, unit cell size, wall thickness, and infill density with modal frequencies. The GA‐based search identified an optimal configuration with a beam width of 18 mm, unit cell size of 9.83 mm, wall thickness of 1.47 mm, and infill density of 20.37%. This design achieved a consistent average frequency of ~1415 Hz in both experimental and simulated environments, validating the accuracy of the surrogate model and robustness of the test setup. The findings emphasize the dominant influence of beam width and wall thickness on frequency augmentation and demonstrate the effectiveness of metaheuristic algorithms, particularly the crossover‐driven genetic search dynamics, in optimizing vibration‐sensitive lightweight structures.
N et al. (2026) studied this question.