October 5, 2022

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This material senses its own motion.  Amazing technology used by scientists

This material senses its own motion. Amazing technology used by scientists

MIT researchers have developed a method for 3D printing materials whose properties enable them to sense how they move and how they interact with their environment.

To achieve this goal, they were helped by the use of 3D-printed materials, which were then placed in a network of channels filled with air during the printing process. By measuring pressure changes in these channels due to compression, bending or stretching, researchers can obtain observations about the movement of the material.

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Such a method can be used, for example, to embed sensors in architectural materials, that is, those whose properties are programmed through shape and configuration. As a result, it should be possible to control factors such as hardness and strength. An example is the case where a denser network of cells creates a more solid structure.

It is not difficult to imagine a scenario in which this technology is used to create flexible and soft robots with built-in sensors and to produce smart wearable devices. These types of tools provide information about, among other things, how the user moves or interacts with the environment.

This 3D printed material can be used in many ways

How did all this happen? Team members used 3D printing to embed air-filled ducts directly into the trusses that make up the reticular structure. When this structure is subjected to pressure from the outside, the channels deform and the volume of air inside them changes. Scientists can measure the corresponding pressure difference using commercially available pressure sensors. In this way, information is collected about how the material is deformed.

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The capabilities of a 3D-printed flexible robot capable of performing many movements, including bending, twisting and stretching, were tested. The machine performed a series of movements for more than 18 hours, and the researchers used the data collected by the sensors to train a neural network that could accurately predict the robot’s movement. The sensors turned out to be surprisingly accurate, which is of course very good news.