AI, the golden solution to save steel?

AI is still evolving, but it already offers businesses many benefits that save millions of dollars every year.

The manufacturing industry has always been a pioneer when it comes to developing and implementing new technologies in order to adopt technologies that help increase productivity while reducing operational costs. Technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) and other advancements have already found their way into the heavy steel industry to drive down production costs.

Steel is an essential metal used for everything from construction to the automotive industry to almost every other industry. The latest technologies allow manufacturers to make real-time adjustments based on accurate data.

Introducing an AI solution designed to help improve steel production is not an easy process and can take months or even years to complete. The main problem is getting the correct data needed to train the machine learning (ML) model.

This data includes the chemical and physical properties of the production materials and the final steel product. In the case of steel, for example, the data includes the thickness of the final product and the temperatures during the various stages of the manufacturing process.

The predictions made by traditional tools are not very precise. Steel companies need to turn to AI.

Steel fabricators need to employ experts who can measure the right data and understand how measurements help improve processes. In other words, a data scientist is needed to filter the different types of data and choose the most relevant ones.

This data covers all chemical and physical properties of the materials used during production. When everything is set up correctly, the AI-powered tools can measure steel thickness, temperatures needed to weld various parts, and overall power consumption.

The system needs accurate and relevant data

For steel production to improve, the machine learning model must be fed with the right data. If older machines are present, the installation of IoT sensors is required on each unit to generate massive amounts of operational data. Most newer machines have built-in sensors, making them easier to implement.

However, since most machines are older, you can connect all machines to a centralized system once the IoT sensors are installed. Installing IoT sensors on obsolete machines is not without challenges. You need to make sure the sensors are collecting the right data to get insights and improve operation. The AI ​​solution needs time to learn how the machines work in order to establish a baseline for the data it generates. When a machine reaches higher temperatures or vibrations, it triggers alarms to notify engineers of a problem. But sometimes users can’t shut down a machine because it’s a bit hot – machines often warm up slightly. They need to determine how much the temperature is too high.

In this case, it is important to involve people who have worked with these machines for years and who know what the important signals are.

Data scientists must thus qualify the data and obtain precise predictions on the mechanical properties of steel on the basis of the data received.

Data analysis also makes it possible to optimize chemical formulas in order to use as few raw materials as possible to produce steel with the required mechanical properties. Models can also be created that can detail how, for example, leftover materials should be moved to different products, to further reduce waste.

AI in the steel industry also helps optimize the supply chain, one of the most important factors when running a steel fabrication plant. IoT sensors help identify potential issues early on to prepare a better plan for the future.

AI-powered solutions analyze vast amounts of data that humans would take decades to process for increased efficiency and reduced costs.

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