Decoupling stiffness and peak moment via hierarchical snapping structures designed with machine learning

Abstract

This work presents the hierarchical tape spring that uses vein topologies to enable novel structural properties that are essential for architected materials with snapping and phase transition capabilities. Such novel structural properties include bending-sense-independent snap-through with energy dissipation, and the decoupling of initial bending stiffness from peak moment. By leveraging an FEA-generated dataset of mechanical performance metrics, we employed a data-driven design approach from literature involving the use of a quadratic regression machine learning model to predict the mechanical behavior of various hierarchical tape springs. The machine learning model was then used as an objective function in a genetic algorithm that searched for hierarchical tape spring designs with user-specified combinations of peak moment and bending stiffness. Designs discovered with the genetic algorithm were validated with FEA, the results of which revealed that the decoupling of bending stiffness and peak moment was made possible by moving transverse veins along the length of hierarchical tape springs with longitudinal veins. If the transverse veins were close to the center line, the tape generally had a higher peak moment, if there were multiple transverse veins near the center line, the hierarchical tape would have a lower bending stiffness with a comparable peak moment. Finally, to prove that the hierarchical tape springs could be fabricated and tested, three designs were chosen and fabricated with a custom vacuum forming rig and tested via four-point bending. Experimental trends were similar to those observed from finite element results.

Keywords

• Machine Learning
• Tape Springs
• Architected Materials
• Snap-through instabilities