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.
Ferritico helps the customer to define what, volume and granularity level of data to be collected, provides data collection automatization capabilities and structures the collected data in databases for the purpose of machine learning model development enabling steel process optimization.
Generic machine learning models predicting steel thermodynamical process and mechanical properties
Customized models considering customer local process environment variables. Implemented to support optimization of customer steel process
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
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