Ferritico
Ferritico provides an AI-driven virtual steel lab that predicts microstructure, hardness, and heat-treatment outcomes before physical trials. The cloud platform generates detailed Continuous Cooling Transformation (CCT) diagrams in seconds from steel chemistry inputs, helping metallurgists reduce costly trial-and-error and accelerate alloy development.
- Manufacturing / Industry 4.0
- Artificial Intelligence
- New Materials
- Data & Analytics
- Software Only
- Industrial Automation
Funding
Funding not disclosed
across 1 round
Founders
Founder details are not available yet.
Product
Problem
Steel manufacturers face significant challenges in optimizing complex production processes, where small variations in raw materials, temperature, and chemistry can lead to costly defects and inconsistent product quality. Traditional trial-and-error methods for developing new steel grades are slow and expensive, requiring extensive physical testing and delaying time-to-market for innovative products.
Solution
Ferritico provides an AI-driven steel innovation platform that digitizes and accelerates the research, development, and production optimization cycle for steelmakers. The cloud-based platform combines metallurgical expertise with data-driven machine learning to predict steel behavior before any heat treatment, enabling virtual experimentation with alloy compositions and process parameters. Metallurgists and process engineers can generate detailed Continuous Cooling Transformation (CCT) diagrams in seconds, predicting phase evolution and hardness for various cooling rates. The platform securely incorporates proprietary experimental data to refine predictions for specific steel grades, reducing the need for costly physical trials and shortening development timelines.
Target Audience
Primary customers are steel manufacturers, including integrated producers and mini-mills, as well as R&D teams within the steel industry focused on developing new high-performance steel grades.
Features
- CCT module that generates detailed Continuous Cooling Transformation diagrams from steel chemistry inputs, predicting ferrite, pearlite, bainite, and martensite formation along with transformation temperatures
- Additional simulation modules for Time Temperature Transformation (TTT) diagrams, tempering effects on microstructure and hardness, hardenability, and surface treatments including carburizing and nitriding
- Hybrid prediction approach combining fundamental metallurgy with advanced machine learning models
- Integration-ready API that enables seamless connection with existing plant systems and software
- Interactive collaboration tools that allow teams to tweak parameters, run what-if scenarios, and compare alloys or process conditions in real time
- Data security and privacy features supporting secure incorporation of proprietary experimental data
- Sustainability benefits including reduced material waste, lower energy use, and a smaller development carbon footprint