A full ladle of liquid steel, seen here at the ArcelorMittal Dofasco plant in Hamilton, weighs about 318 tonnes, and can be heated to about 1,600 C. Dofasco wants to fully automate its ladle metallurgy process – the stage when trained operators manually add the ‘secret sauce’ to liquid steel before it is cast into numerous grades of steel slabs for construction, car-making and packaged goods. Instead, digital sensors could consistently determine the precise temperature at which to add the right ingredients to produce the desired grade of steel
Dofasco wants to improve its ladle metallurgy process, a key step in steel-making, while Algoma eyes the potential to automate product development. In both cases, the companies look to artificial intelligence and machine learning to help them set new, world-beating standards of efficiency, quality, energy savings and generate as-yet unimagined innovations.
Steel-making is a science but still relies on experienced human operators at key stages of production. But what if “smart” technology replaced manual tasks with digital sensors that consistently update information and reveal insights impossible to detect with the trained eye – or brain ? Answering the “what if” question is a top priority for Canadian steel giant ArcelorMittal Dofasco as it strives to compete globally. To speed the hunt for answers – when Canadian steel already is under siege from U.S. tariffs in a burgeoning global trade war – the Hamilton-based manufacturer hopes to join forces with innovative startups, university researchers and even a competitor, Essar Steel Algoma Inc., to promote a new generation of manufacturing.
“Traditionally, the research and development of a product in steel-making is a slow process, In future, Algoma could apply self-learning algorithmic models to assess multiple variables at once – beyond the capacity of any experienced technician. As a result, the product development cycle could take just 15 days to two or three months.”
Pramod Shukla, chief operating officer of Algoma, in Sault Ste Marie, Ont
In future, he says, Algoma could apply self-learning algorithmic models to assess multiple variables at once – beyondContinue reading→
