CONSULTANCY
MACHINE LEARNING
STRATEGY
Ferritico conducts pre-studies to identify steel development, manufacturing and implementation process machine learning opportunities. We provide digitalization recommendations, i.e. how out-of-the-box and customized machine learning models can be combined to enhance steel product quality metrics, increase manufacturing output or to track the cause of product defects.
DATA
MANAGEMENT
Customer empirical data digitalization and database development
Open literature data collection and local data set integration
Synthetic data generation using i.a physical modelling and first principles calculations
Data quality control design and technical implementation
Data pipeline solution development, i.e. automize process of converting raw process data
Data storage, e.g. develop and host databases and search capabilities for customer datasets.
NOVEL STEEL
DEVELOPMENT
Novel steel recipe development based on requirement specification. Ferritico team stand-alone or as an integrated part of customer R&D to educate in ICME methodology and Ferritico machine learning simulation work-flow.
Steel optimization, i.e. fine tuning recipe to boost properties, reduce cost or decrease environmental impact
Alloy element replacement projects, i.e. refine recipe in order to reduce cost or dependency on critical alloying elements while maintaining mechanical properties.
Production ramp-up consultancy projects, i.e. simulation supported process to maintain properties when going from pilot to production scale
FEDERATED
LEARNING
The Ferritico federated learning solution helps our customers to leverage local steel data sets without having to share and distribute data. Ferritico central prediction models are reinforced through local data consumption at the customer site and then deployed as web services. The federated learning solution enables customer collaboration without data sharing and provides the collaborators a boosted model at license discount in relation to model contribution.
FINITE ELEMENT METHOD
MATERIALS DATA
Finite Element Method (FEM) simulation quality correlates with input materials data quality. Improved FEM simulations implies less trial-and-error during design and enables product performance and sustainability optimization. Ferritico provides materials data API:s where steel materials data can be consumed as a service for the purpose of boosting FEM simulations
