One of the major challenges in steel production is to increase the quality of steel and reduce unnecessary waste due to cracks. To help Uddeholm, a leading steel producer, overcome this challenge, we developed a powerful machine learning-based solution that helps to identify production errors, refine processes and improve profitability. 

By evaluating the possibilities of new technologies like artificial intelligence and machine learning, we started helping Uddeholm tackle one of their most pressing challenges: ensuring a high-quality steel product.

Cracks in steel impact profitability

Steel manufacturers cannot assess the quality of their output until the end of the manufacturing process, at which point they can discover cracks in a large amount of the finished product. The damaged steel must be melted and fed back into the process, wasting enormous amounts of energy, time and money.

Uddeholm wanted to improve their manufacturing process and reduce cracks in completed steel to avoid significant costs and wastage.

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accuracy in predicting cracks in steel

Predicting cracks in steel with over 70% accuracy

Together with Uddeholm, we developed a high-powered machine learning model that could predict—with over 70% accuracy—where and when cracks would occur.

We selected three steel products, data sources and analysis methods to analyze Uddeholm’s data as it related to quality and relevance. Our approach used big data and an Internet of Things (IoT) platform to capture and handle relevant data and apply machine learning and advanced analytics to find new insights and gain the necessary knowledge to improve the quality of completed steel.

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Better processes, less waste

Analyzing the data and reverse engineering of the machine learning models enabled Uddeholm to pinpoint the cause of quality issues, significantly improving the ability to eliminate or reduce damaged steel. Uddeholm is now able to adjust its manufacturing process to reduce the waste associated with cracked steel.

This project is an important first step toward digitizing their steel manufacturing production process and business.