100 % Process stability in injection molding with Federated Learning
As a manufacturer of injection molding machines, you will become a valuable partner to your customers with a digital solution for optimizing process stability. This will automatically take into account the design of the machine depending on the material used. With the number of machine and material pairings in the field, does that seem impossible?
Learn how an injection molding machine manufacturer optimizes process stability for all machine and material pairings in the field in our case study.
The federated learning use case for plastics and rubber engineering machine builders
Your customers hesitate when you talk about implementing digital solutions that require sharing production data? Even if your customers do not share sensitive production data, you can enable intelligent control of their injection molding process in real time. This involves training an individual model for each of your customers that takes into account the design of the machine depending on the material used. This allows you to offer your customers a substantial competitive advantage, even with increasing recyclate content or decreasing batch sizes.
Your Key Benefits
- Automated clustering of comparable machine/material pairings across company boundaries
- Enable intelligent control of the injection molding process in real-time
- Offer customer-specific improvements directly at the machine
- Leverage insights across company boundaries
- Secure collaboration through data sovereignty of your customers
- Injection molding machines 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 an injection molding machine manufacturer uses Katulu Federated Learning to optimize process stability for each customer's machine and material pairings across company boundaries, creating a strong competitive differentiator.