Optimization of shape memory origami structures: modeling, simulation, and 4D printing [* Cross-Reference *]

Graded structures were introduced, varying their geometric characteristics throughout the volume to explore optimized distributions of material and mechanical properties in response to loads. These variants represent a significant advancement in customizing functional properties, enabling structures to meet specific performance requirements with even greater precision. The optimization algorithms employed include particle swarm optimization, genetic algorithms, and the Sunflower algorithm, all applied to refine structural parameters and identify the best solutions in multi-objective optimization.

This study also underscores the emerging importance of shape memory materials in additive manufacturing, particularly for applications that benefit from inherent structural adaptability. The integration of graded origami structures with advanced material technologies represents a promising frontier for future innovations in engineering and design, with the potential to revolutionize how functional structures are conceived and implemented across various fields.

Quantitatively, the study achieved a hypervolume of 0.88 using the MOPSO algorithm for mass minimization, indicating a broad and effective search for optimal solutions. Additionally, experimental results demonstrated a 99.3% height recovery in the Miura-ori structure after deformation and heating, confirming the robustness and applicability of these structures in real-world scenarios. ***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts.

Please refer to doi.org/10.1007/s10999-025-09793-1 for the original and latest version of the dataset and data downloads***