Data-Driven Optimization of Aperiodic Beams for Broadband Vibration Suppression

Background/Introduction: Variable cross-section beams are widely used in flexural wave isolation. Conventional periodic designs can only achieve vibration isolation within specific frequency bands, which limits their functionality. Purpose: An optimized design model for broadband high-stiffness non-periodic variable-section beams is proposed using machine learning (ML) and genetic algorithms (GA). Methods: The model integrates GA and multi-objective GA, combined with ML and the spectral stiffness matrix method (SEM), to enable efficient and precise design of variable-section non-periodic beam structures. Results: The results show that the optimized beams can simultaneously suppress vibration across 500 –4000 Hz and 4000 –9000 Hz. When customizing specific band gaps, considering multi-objective optimization allows the bending stiffness of the aperiodic beam to increase by up to three times. Conclusion: The proposed model enables efficient design of high-performance vibration isolation beams for engineering applications.