Mechanical Metamaterials for Handwritten Digits Recognition

The increasing needs for new types of computing lie in the requirements in harsh environments. In this study, the successful development of a non-electrical neural network is presented that functions based on mechanical computing. By overcoming the challenges of low mechanical signal transmission efficiency and intricate layout design methodologies, a mechanical neural network based on bistable kirigami-based mechanical metamaterials have designed. In preliminary tests, the system exhibits high reliability in recognizing handwritten digits and proves operable in low-temperature environments. This work paves the way for a new, alternative computing system with broad applications in areas where electricity is not accessible. By integrating with the traditional electronic computers, the present system lays the foundation for a more diversified form of computing. A non-electrical mechanical metamaterial to achieve a neural network empowering handwritten number recognition without an electricity supply is presented. By overcoming the challenges of mechanical signal transmission efficiency and intricate layout design, a mechanical neural network based on kirigami metamaterial is achieved. This work provides an effective method for various science and engineering applications where electricity is not accessible.image