Evaluate
Define ML objective
Specify what the Machine Learning model should learn and predict.
Move through every stage of automation with one connected approach.
Explore proven use cases for testing, validation and production optimization.
From initial planning to production-ready systems and automated engineering workflows.
Build reliable virtual commissioning workflows, industrial software and reusable expertise.
Tools for the Digital Factory
RF::SUITE
One connected platform for engineering, simulation, validation and production intelligence.
Explore RF::SUITE →
Choose the tools your workflow needs and connect them within one consistent engineering environment.
We combine simulation, virtual commissioning and industrial software expertise across demanding production environments.
From individual machines to complex production lines: we connect engineering, simulation and operation throughout the entire lifecycle.
Explore IndustriesWe participate in national and international research projects focused on digital engineering, automation, artificial intelligence and sustainable production systems.
Resilient, sustainable and adaptable industrial production systems.
→ 02 Research Project REDRIVESResilient drive technologies and robust industrial production processes.
→ 03 Research Project DIGIKLEBDigital methods for connected adhesive processes and quality assurance.
→ 04 Research Project GENIUSIntelligent engineering methods for data-driven manufacturing systems.
→Learn more about EKS InTec, our locations, career opportunities and how to get in touch with our team.
Generate targeted training data for Machine Learning models when real data is limited, expensive, or incomplete.
Generate training data
Represent rare cases
Improve data quality
Different variants and conditions can be systematically covered.
Training data is available without waiting for real production events.
Models train on more diverse and complete datasets.
All relevant states and parameters are defined and traceable.
How it works
Simulation enables systematic generation of training datasets for Machine Learning applications. Rare, critical, and edge-case scenarios can be represented reliably ??? especially when real operational data is scarce or costly to collect.
Evaluate
Specify what the Machine Learning model should learn and predict.
Simulate
Set up simulation variants covering normal and edge cases.
Validate
Run simulations and collect labeled training data automatically.
Explore more
Discover additional simulation-based applications
across engineering, operation and optimization.