Conveners
AI & Machine Learning Applications
- Valentina Colla (Scuola Superiore Sant'Anna)
AI & Machine Learning Applications
- Christine Gruber (K1-MET)
Description
6.1.1
In the ongoing digital transformation of the steel industry, Artificial Intelligence is more and more
integrated into the automation of the production processes. However, the true potential of AI
technologies can only be realized if preconditions to data quality are given, and domain expertise is
effectively digitized.
This presentation explores the application of AI technologies—such as...
Quality Control is an established process for all metals manufacturers. However, identifying a critical quality deviation at the finished product stage can be cost-intensive. Not only is the original order affected by delays due to late deallocation, but the reallocation of a finished product to a prime order becomes improbable, leading to a greater financial loss. Predictive-Prescriptive...
In this paper a distributed auction system intended to provide efficient real-time reactive scheduling for flat steel industry is introduced. We focus on the context of parallel finishing lines where several equipment units can be used to process the materials. When a severe disruption takes place, it is necessary to provide a feasible reschedule in a timely manner.
Regarding the scheduling...
To improve operational efficiency and minimize downtime, it is crucial to understand the causes leading to process defaults in the steelmaking industry. This study presents a generalized framework applicable across a wide range of metal-forming processes, which is based on an AI-powered solution designed to predict and classify breakages caused by process anomalies. The proposed solution...
Quality control in zinc-coated steel coils is critical in industries such as automotive and construction, where defects in zinc coating can significantly impact product durability and safety. Traditional manual inspection methods are prone to errors and inconsistencies, especially in high volume production environments.
This paper compares the performance of two artificial intelligence...
The Electric Arc Furnace (EAF) plant in the steel industry presents significant health and safety risks for workers due to extreme temperatures, toxic fumes, and potential operational hazards. To enhance workplace safety, this study proposes an advanced automated system that leverages cutting-edge IT and AI technologies to improve process monitoring and risk prevention.
A key objective of...
Achieving autonomous operation requires automation and digitalization systems with advanced control and learning capabilities. This paper discusses key elements on the pathway to autonomous blast furnace and DR operation:
Autonomous decision-making for ironmaking operations leverages sophisticated technologies, including AI and ML, to measure, control, and optimize key parameters of...