Case Study for Machine Tools

Predictive Maintenance for Machining Technology with Federated Learning

As a machine manufacturer for machining technology, you would like to offer your automotive customers an industry-specific predictive maintenance solution. As a high-turnover customer segment, the automotive industry has complex requirements in terms of machine availability, repeatability, and efficiency that make implementation challenging.

Learn in our case study how a manufacturer of milling machines meets these requirements through Federated Learning and documents them in a legally compliant manner.

The Federated Learning Case Study for Machine Builders in the Machine Tool Sector

You want to avoid machine breakdowns through predictive maintenance, and optimize your approach to proving contractual requirements for your machines. In addition, you are increasingly faced with demands for transparent data ownership regulation in the context of digitalization. With Katulu Federated Learning, predictive maintenance can run completely decentralized in the respective production sites of multiple automotive customers. You will be able to increase machine availability and document it in an audit-proof manner - without sensitive data leaving the production sites.

Your Key Benefits

  • Increase predictive accuracy of machine failures with custom AI models
  • Increase efficiency & precision of power cutting with real-time monitoring
  • Prove machine availability guarantees in a legally secure way
  • Protect your customers' data sovereignty
  • Integrate customized AI models into existing customer systems
  • Enable AI even without a permanent connection to the cloud
  • Machine tools learn from each other - without learning about each other

In our case study, you will find further information on how you can use the advantages of Federated Learning for your company and strengthen your competitive position in the long term.

Read the case study to learn how Katulu, a machine tool manufacturer, uses federated learning to rethink predictive maintenance using the latest machine learning technology to meet the growing demands of the automotive industry.