Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft...

Area of Science:

• Materials Science
• Robotics
• Biomimicry

Background:

• Soft machines offer potential for unstructured environments but lack adaptability.
• Current soft machines have limited real-time tunability and reprogrammability.
• Biological organisms like octopuses exhibit superior adaptive capabilities.

Purpose of the Study:

• To introduce an encodable multifunctional material for in situ programming of mechanical capabilities.
• To overcome limitations in real-time tunability and reprogrammable properties of soft machines.
• To create a steppingstone toward fully adaptive soft robots and smart interactive machines.

Main Methods:

• Utilizing graphical stiffness patterns to program mechanical properties.
• Independently switching digital binary stiffness states (soft or rigid) of constituent units.
• Employing a simple auxetic structure with elliptical voids.

Main Results:

• Demonstrated in situ and gradational tunability of mechanical qualities.
• Achieved versatile shape-shifting, shape-memory, and tunable stress-strain response.
• Showcased tunable Poisson's ratio and application-oriented functionalities like energy absorption and pressure delivery.

Conclusions:

• The digitally programmable material enables versatile mechanical programming without external infrastructure.
• This innovation paves the way for developing multienvironment soft robots and interactive machines.
• The material mimics biological adaptability, enhancing soft machine performance.