Use case

How One Global Steel Producer Proved AI Value Quickly — Without Overcomplicating the Pilot

Name: CamelCase Collective
Price range: $

This article describes how a global steel producer evaluated Fero Labs’ explainable AI through a focused proof-of-concept pilot rather than a broad digital transformation. The pilot targeted a common steel mill challenge: manual machine learning model retraining that consumes hundreds of engineering hours each year. By validating model accuracy, enabling real-time retraining, and removing ongoing maintenance, Fero Labs helped the producer demonstrate clear ROI before expanding into additional use cases such as alloy optimization, defect diagnostics, mechanical property prediction, and process optimization. The post outlines a practical, repeatable approach for steel producers, manufacturers, and consultants seeking to deploy industrial AI, scale engineering expertise, improve efficiency, protect product quality, and support more sustainable steel production—without disrupting existing workflows.

Read Article

Caster_Breakouts

Diagnose Root Causes of Continuous Caster Breakouts in Minutes

Learn how to diagnose root causes of continuous caster breakouts in minutes with Fero Labs' AI solution. Traditional methods like Six Sigma take months and extensive manual analysis across disparate data systems. Our machine learning platform identifies complex patterns across hundreds of variables in real-time, reducing breakout incidents by 30-50% and unplanned downtime by 15-20%. Essential reading for metallurgists and process engineers seeking faster, more accurate solutions to costly steel production disruptions.

Read Article

Slag-Optimization

Flexible Recipes for Slag Optimization With Fero Labs AI

Discover how Fero Labs' AI technology transforms slag management for steel manufacturers. Learn practical strategies to optimize slag chemistry, improve desulfurization efficiency, reduce flux consumption, and increase metallic yield - all while minimizing operator effort. This comprehensive guide helps metallurgists, meltshop managers, and process engineers implement data-driven slag optimization for each heat, resulting in superior steel quality, reduced energy consumption, and significant cost savings. Explore how predictive machine learning models are replacing outdated rule-based approaches to slag management, delivering measurable ROI within months of implementation.

Read Article

Steel

Use Case: Caster Optimization for Surface Defect Reduction

Use this technical AI use case playbook to minimize surface defects during continuous casting by adaptively controlling casting parameters on a batch-by-batch basis in real time. Continuous casters can expect a 18% decrease in scrap rates, and up to 40% reduction in average surface crack lengths.