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The team from Tohoku University in Japan developed a ‘contact control system’ aimed at reducing contact with friction-damaged areas in machine parts. Although so far only tested in lab experiments, they believe it could eventually help many types of machinery run more smoothly, including infrastructure such as wind turbines.
“This could shift the design strategy of mechanical systems away from the traditional approach of developing new and superior materials to developing surfaces that can actively adapt to reduce the damage,” said Professor Motoyuki Murashima.
Mechanical systems where moving parts come into regular contact are prone to damage because of friction.
The research focused on the potential of new materials with morphing surfaces, which can change depending on the environment they operate in. These materials are being developed by several research groups to mimic the flexibility found in living systems, such as leaf surfaces that change in response to variations in humidity. One engineering example, previously developed by Murashima and colleagues, is a surface composed of a diaphragm supported by hard substrate, with changes in stress pressure altering the surface morphologies.
The team developed an AI-driven procedure in which sensors analyse the friction between two surfaces. Having detected where damage is occurring, the procedure can then use the morphing capacity of the surface to minimise the frictional contact with damaged regions.
“This is the first research in the world to use artificial intelligence to control the shape of morphing surfaces and successfully detect the position of damage on interacting surfaces,” said Murashima.
Through analysis and adjustments in simulated tests, the researchers were able to reduce the fluctuating friction caused by contact between affected parts of the material.
The proof-of-concept system used discs spinning within a cylinder. The next step will be to move closer to real-life situations, the researchers said, such as industrial machinery.
The aim is to allow a wide range of machinery to operate with less routine wear and damage, achieving longer lifespans and cost savings due to less frequent part replacements.
“An important next step is to develop more sophisticated learning and control algorithms that will reduce the time needed to learn the characteristics of the analysed surfaces, and therefore achieve more refined and speedier control that prevents damage,” said Murashima.
The work, published in Tribology International, was a collaboration with Nagoya University in Japan and the Korea Photonics Technology Institute in South Korea.
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