Process Digital Twins for Thermoprocessing with Federated Learning
As a manufacturer of thermoprocessing equipment, you want to enable your customers to operate existing equipment more cost-efficiently through process simulation. Since all customers have customized system solutions with different assemblies, implementing customer-specific digital twins with conventional machine learning technology is challenging.
Learn in our Case Study how a manufacturer of customized thermal process plants enables the simulation of thermal processes with digital process twins and Federated Learning.
The Federated Learning Use Case for Mechanical Engineering Specialists in Metallurgy
Your customers want to measurably improve the cost efficiency of their existing thermoprocessing plants. With your digital process twins, they can simulate manufacturing processes in advance with plant- and user-specific parameters. For precise predictions, you want to incorporate melt process knowledge from as many industrial applications as possible. Win customers by letting them benefit from improved manufacturing costs without sharing their raw data to do so.
Your Key Benefits
- Enable cost-efficient planning through simulations with Digital Twins
- Offer each customer his individual process twin
- Make Industry 4.0 possible with Machine Learning in special purpose machine building
- Benefit from automatic clustering of AI models on assembly level
- Win customers with transparent data sovereignty
- Thermal process plants learn from each other - without learning about each other
Read our case study to learn more about how you can leverage these benefits to strengthen your competitive position.
Learn how a thermoprocess equipment manufacturer uses Katulu Federated Learning to provide digital twins for thermo process simulation with maximum data sovereignty.
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