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Accelerating Product Chemistry Refinement Using Explainable Machine Learning
There is significant hype around artificial intelligence (AI)/machine learning (ML) and its implications for manufacturers. Unfortunately, this hype has not translated into replicable return on investment, impeding widespread integration of this technology into the daily workflows of plant engineers. Many companies are left with failed pilots and digital transformation initiatives that do not deliver results.
This webinar describes how Gerdau has successfully navigated beyond the conceptual phase of digital transformation and achieved significant cost-savings results, leading them to deploy Fero Labs to many additional plants. They use machine learning to not only accelerate product chemistry refinement but also to continuously track realized cost improvements. Gerdau’s plants have been leveraging ML to automatically model production data to better define the relationships between process parameters (e.g., chemistry, rolling mill temperatures, product properties) and mechanical results. The use of explainable ML software has accelerated the prediction of mechanical KPIs, enabled accurate simulations of the effects of process changes, and led to the optimization of alloy content of specific grades.
In this webinar, Gerdau and Fero Labs will walk the audience through the metallurgical design process using ML software and highlight the new workflow of plant engineers and operators. Part of the webinar will be devoted to the description of explainable ML and how it complements the existing process optimization toolkit. The discussion will also cover the steps needed to integrate the new technology into the existing IT infrastructure for near-real-time alloy optimization. The session will conclude with a summary of the year-to-date improvements Gerdau has achieved and the resulting reduction in alloy costs of the new product grades.

Moderator: Ramesh Khajjayam, Primetals Technologies

Jul 22, 2020 10:00 AM in Eastern Time (US and Canada)

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