Mechanical Engineering

Improving the efficiency of system development is essential for quickly addressing rapid changes in social conditions, which is why data-driven system design methods based on machine learning are expected to serve as a means to achieve this. In order to further enhance control stability without relying on the nature of data, we proposed a method to learn the control object with a specially structured machine learning model. When using this model as a predictor, solutions to mathematical problems that require optimal control under various conditions (optimal control problems) are mathematically guaranteed to be unique and continuous. Currently, we are working to apply the method to engine control in order to achieve smooth control while improving acceleration and environmental performance.^*2
*2: Jointly developed in part with Toyota Industries Corporation

Reducing machine vibration and noise contributes to comfort in mobility and a variety of other living environments, as well as to product safety. We are advancing research into new vibration-damping structures and materials that efficiently use material resources to revolutionize silence, and into their design theories. For example, by deriving equations for the propagation characteristics of elastic waves inside shell architectures with periodic structures, we enabled the design of bandgap periodic structures that do not propagate vibration in specific frequency bands. We applied this method to design a metamaterial structure using a pulp mold^*3, and combined the metamaterial bandgap effect with material damping to achieve a vibration-damping component that significantly reduces vehicle vibration and noise over a wider frequency range than before.
*3: Jointly developed with TOYOTA AUTO BODY CO., LTD.