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Last updateFri, 05 Jun 2020 6am
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KIT: Servicing Machine Tools with Artificial Intelligence

Researchers of the Karlsruhe Institute of Technology (KIT) have developed a system for fully automatic monitoring of ball screws in machine tools. A camera integrated directly into the nut of the ball screw is used. Based on the image data generated in the process, an artificial intelligence (AI) continuously monitors wear and tear, thus reducing machine downtime.

The maintenance and timely replacement of defective components in machine tools is an important part of the production process in mechanical engineering. In the case of ball screws, such as those used in lathes for precision guidance in the manufacture of cylindrical components, wear has so far been determined manually. "Maintenance is therefore associated with assembly work. The machine then stands still for the time being," says Professor Jürgen Fleischer from the KIT Institute for Production Technology (wbk). "In contrast, our approach is based on the integration of an intelligent camera system directly into the ball screw. This allows a user to continuously monitor the condition of the spindle. If there is a need for action, he is automatically informed.

The new system consists of a camera with lighting attached to the nut of the ball screw, combined with an artificial intelligence to evaluate the image data. While the nut is moving on the spindle, it takes individual pictures of each section of the spindle. This allows the entire surface of the spindle to be analyzed.

Artificial intelligence for mechanical engineering

The combination of image data from ongoing operation with machine learning methods enables users of the system to directly evaluate the condition of the spindle surface. "We have trained our algorithm with thousands of images so that it can now confidently distinguish between spindles with and without defects," says Tobias Schlagenhauf from the wbk, who worked on the development of the system. "By further evaluating the image data, the wear can also be precisely quantified and interpreted. In this way we can distinguish whether discoloration is simply dirt or harmful pitting". During the training of the AI, all conceivable forms of visually visible degeneration were considered and the functionality of the algorithm was validated with new image data never seen before by the model. The algorithm is suitable for all application cases where image-based defects on the surface of a spindle are to be identified and can also be transferred to other application cases.

At the Hanover Fair 2020 from April 20 to 24, KIT will show in Hall 25 Research & Development at Booth C14 what is possible with intelligent spindle monitoring in ball screws. In addition, as in previous years, KIT will be represented with a booth in hall 27 (Integrated Energy, booth L51) as well as at other theme booths.

www.sek.kit.edu

 

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