Ballscrew breakthrough

With technology under development at Karlsruhe Institute of Technology in Germany, machining firms soon may be able to improve the process of spotting ballscrews in danger of failing.

KIT researchers have nearly completed a system for fully automated monitoring of ballscrew drives in machine tools. The system includes a camera with a light source, which attaches to the nut of a drive. As the nut moves on a spindle, the camera photographs each spindle section. These images are evaluated by an artificial intelligence algorithm capable of determining whether they show signs of wear that can lead to ballscrew failure.

A system under development at Karlsruhe Institute of Technology automatically monitors ballscrew wear using artificial intelligence. Image courtesy of Karlsruhe Institute of Technology

Using machine-learning methods, the AI algorithm was trained with thousands of images to distinguish between spindles with and without defects. When training the algorithm, KIT reports that the research team took into account “all conceivable forms of visible (ballscrew) degeneration.” As a result, the software system can tell whether discoloration shown in ballscrew images is simply dirt or harmful pitting, according to the institute, which added that its researchers have substantiated that claim by testing the system with new ballscrew images that the system never had seen.

At present, people interested in monitoring the status of ballscrew drives in machines have two choices: measuring the motor current or the acoustics of the devices, said Tobias Schlagenhauf, a research associate who helped develop KIT’s system. He said when current or sound exceeds a specified threshold, that indicates that a ballscrew is worn. But he pointed out that both monitoring methods have significant downsides.

When measuring motor current, “you can see something only when the component is very worn” and virtually at the end of its life, he said.

As for the acoustic option, Schlagenhauf said, “it’s very hard for a user to find small defects in this signal. Finding small defects is very important because they indicate that the component will fail in the near future.”

He said the automatic KIT system is much better at finding small defects than acoustic- or motor current-based systems.