The timely maintenance and replacement of defective components in machine tools is an important part of the manufacturing process. In the case of ball-screw drives, wear has until now been determined manually.
“Our approach integrates an intelligent camera system directly into the drive, which enables a user to continuously monitor the spindle status,” says Professor Jürgen Fleischer from the Institute for Production Technology at KIT. “If there is a need for action, the system informs the user automatically.”
The system combines a camera featuring light source attached to the nut of the drive, with AI deployed to evaluate image data. As the nut moves on the spindle, it takes individual pictures of each spindle section, enabling the analysis of the entire spindle surface.
Combining image data from ongoing operations with machine-learning methods enables system users to assess directly the condition of the spindle surface.
“We trained our algorithm with thousands of images so that it can now confidently distinguish between spindles with defects, and those without,” says Tobias Schlagenhauf, who helped develop the system. “By further evaluating the image data, we can precisely qualify and interpret wear, and thus distinguish if discoloration is simply dirt or harmful pitting.”
When training the AI, the team took into account all conceivable forms of visible degeneration and validated the algorithm’s functionality with new image data that the model had never seen before. The algorithm is suitable for all applications that identify image-based defects on the spindle surface, and is transferrable to other applications.
