With timely maintenance and replacement of defective components in machine tools being an important part of the manufacturing process, researchers at Karlsruhe Institute of Technology (KIT) in Germany developed a system for automated monitoring of ball screw drives in machine tools.

In the case of ball screw drives, such as those used in lathes to precisely guide the production of cylindrical components, wear is determined manually.

“Maintenance is therefore associated with installation work, which means the machine comes to a standstill,” says KIT professor Jürgen Fleischer.

He adds that the researchers’ approach integrates an intelligent camera system directly into the drive, which enables a user to continuously monitor the spindle status. If there is a need for action, the system informs the user automatically.

Fleischer adds that the new system combines a camera with light source attached to the nut of the drive and artificial intelligence (AI) that evaluates the 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 directly assess 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. By further evaluating the image data, we can precisely qualify and interpret wear and thus distinguish if discoloration is simply dirt or harmful pitting,” says KIT’s Tobias Schlagenhauf, who helped in developing the system.

He explains that, when training the AI, the team took account of 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, he concludes.