Digital synthesis of free-form multimaterial structures for realization of arbitrary programmed mechanical responses

Abstract

Significance Creating structures to realize function-oriented mechanical responses is desired for many applications. Yet, the use of a single material phase and heuristics-based designs may fail to attain specific target behaviors. Here, through a deterministic algorithmic procedure, multiple materials with dissimilar properties are intelligently synthesized into composite structures to achieve arbitrary prescribed responses. Created structures possess unconventional geometry and seamless integration of multiple materials. Despite geometric complexity and varied material phases, these structures are fabricated by multimaterial manufacturing, and tested to demonstrate that wide-ranging nonlinear responses are physically and accurately realized. Upon heteroassembly, resulting structures provide architectures that exhibit highly complex yet navigable responses. The proposed strategy can benefit the design of function-oriented nonlinear mechanical devices, such as actuators and energy absorbers.

Modeling and Design of Zero‐Stiffness Elastomer Springs Using Machine Learning

“…Future work could focus on extending these methods for other types of QZS materials, for example, elastomer metamaterials with curved beam elements whose deformation mechanics may be different than that of the chi springs. [ 5,42,51 ]…”

“…Future work could focus on extending these methods for other types of QZS materials, for example, elastomer metamaterials with curved beam elements whose deformation mechanics may be different than that of the chi springs. [5,42,51] There are key advantages to using an ML model with quantified uncertainty when training the ML model from experimental data. Experiment-driven training is advantageous owing to its intrinsic ability to capture the physics involved in the measured behavior, such as the effect of self-contact, as well as the material or geometric variations that may result from the manufacturing.…”

Encoding reprogrammable properties into magneto-mechanical materials via topology optimization

“…To support the numerical findings, we fabricate the optimized unit design in Scenario 2 using a hybrid 3D printing and casting approach 45,46 (shown in Fig. 5a) and experimentally validate its switchable deformation modes under compression.…”

Magnetic poles enabled kirigami meta-structure for stable mechanical memory storage with high information density and structural robustness

“…Mechanical meta-structure (or meta-material) have been demonstrated with various exotic properties beyond natural materials [1][2][3][4] . Pre-assigned with unique structural units, mechanical metamaterials can be integrated with mechanical intelligence (such as snap-through based instability 5,6 , bi/multi-stability [7][8][9] , topological (re)programmability [10][11][12] ) and/or materials intelligence 3,13,14 (for example combining with thermo-/electro-/magneto-actuated materials including liquid crystal elastomer 15,16 , shape memory alloy/polymer [17][18][19] , ferromagnetic material [20][21][22][23] , hydrogel 24,25 , dielectric materials 26 etc.) to achieve tunable and (re)programmable mechanical properties 27,28 , morph target shapes [29][30][31] , imitate electrical circuits 32,33 , perform logic computation 27 , encrypt/process simple information 34,35 , sense/intercept external environment conditions 36 , and/or act as multi-functional robotic structural platforms 37 .…”