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Smart machine components assist availability by alerting users to damage and wear

Scientists at the United Technologies Research Center and UConn used advanced additive manufacturing technology to create smart machine components that alert users when they are damaged or worn.

The key to this innovation is the use of an advanced form of 3D printing called direct write technology. Unlike conventional additive manufacturing, which uses lasers to fuse layers of fine metal powder into a solid object, direct write technology uses semisolid metal 'ink' that is extruded from a nozzle. The viscosity of the metal ink looks like toothpaste being squeezed from a tube.

This process allowed the UConn-UTRC scientists to create fine lines of conductive silver filament that could be embedded into 3D printed machine components while they were made. The lines, which are capable of conducting electric current, act as wear sensors that can detect damage to the part.

Here's how they work. Parallel lines of silver filament, each coupled with a tiny 3D-printed resistor, are embedded into a component. The interconnected lines form an electrical circuit when voltage is applied. As lines are embedded deeper and deeper into a component from the surface, each new line and resistor are assigned an increasingly higher voltage value. Any damage to the component, such as wear or abrasion caused by friction from moving parts, would cut into one or more of the lines, breaking the circuit at that stage. The more lines that are broken, the greater the damage. Real time voltage readings allow engineers to assess potential damage and wear to a component without having to take an entire machine apart.

"This changes the way we look at manufacturing," says Sameh Dardona, Associate Director of Research and Innovation at UTRC, which serves as the innovation engine for United Technologies Corp. "We can now integrate functions into components to make them more intelligent. These sensors can detect any kind of wear, even corrosion, and report that information to the end user. This helps us improve performance, avoid failures, and save costs."

The UConn-UTRC team was able to embed sensor lines that were just 15 microns wide and 50 microns apart. That's much thinner than an average human hair, which is about 100 microns. This allows detection of very minute damage.

More detailed information about fabrication of the wear sensors can be found in an article in Additive Manufacturing.



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