Articles

Short-term scheduling in steel is still often handled process by process, with melt shop, caster, and rolling mill planned in isolation. This article shows how modern automatic scheduling systems using MILP, heuristic rules, and machine learning-based adaptation synchronize all production steps into one continuous flow, enabling reactive rescheduling, higher direct charging rates, reduced caster idle time, and measurable energy savings.

Can you effectively combine the strength of human insight with automated precision to enhance efficiency? This article examines mid-term planning, the limitations of manual scheduling and full automation, and how user-guided automation using open-box MILP models provides a balanced, flexible approach to production scheduling in the steel industry.

The integration of digital manufacturing techniques is revolutionizing the steel industry. This article examines the shift from mid-term to reactive short-term production scheduling, advocating for a hybrid approach using open-box MILP models augmented with user guidance.

From melting to hot rolling: increasing the reheat furnace charging temperature by schedule optimisation. Examines how digital twins, virtual slab yards, and MILP-based scheduling can synchronise production planning across fully integrated steel mills to increase energy efficiency and reduce CO₂ emissions.

Increasing yield by optimising production schedules for a minimum of grade transitions. Explores how modern mini mills can apply Mixed Integer Linear Programming to reduce costly grade transitions, with AI-based quality prediction models feeding into the scheduling optimiser.

How producers can make their own data BIG and leverage the benefits of Big Data analysis. Defines Big Data in the context of the steel industry, explores the types of data generated in steel mills, and examines how the SST data platform enables real-time AI applications.

Part of the AIST Digitalization Applications 101 learning module. Defines Big Data in the context of the steel industry, outlines types of data, benefits of analytics, and practical challenges. Developed by the AIST Digitalization Applications Technology Committee.

Interview feature: Jacqueline and Michael Peintinger outline their plans to develop and build an American subsidiary of Smart Steel Technologies, and discuss the role of AI and Big Data in the transformation of the North American metals industry.

How transparency through Industry 4.0 tools drives changes in company culture. Argues that adoption of Industry 4.0 tools is driven by trust and acceptance of the people using them. Data visibility, material genealogy, and statistical process control are key enablers.

Applying QuinLogic’s rule-based Quality Execution System in the manufacturing of press hardening steel, bringing benefits through optimisation of the hot stamping production process. Covers rule management, live quality monitoring, and laboratory data integration.