Falkonry & ArcelorMittal Selected for Smart Manufacturing Project
Strategic Research Institute
Published on :
18 Feb, 2022, 5:14 am
Cupertino California US based leader in time series AI Falkonry has been selected for an innovation project by CESMII, The Smart Manufacturing Innovation Institute, in collaboration with ArcelorMittal Nippon Steel Calvert and ArcelorMittal Global R&D. The project was selected under the CESMII Roadmap Projects RFP3 initiative which aims to accelerate the adoption of sustainable and smart manufacturing practices in production operations.
Falkonry is collaborating with ArcelorMittal to develop a strip break classification system in their Calvert, AL cold rolling tandem mill. One of the issues in the cold rolling of sheet steel, strip breakage, results in yield loss due to line stoppage, re-work, and may also cause damage to equipment. The objective of this project is to automatically classify strip break events using time series AI and machine learning and provide their explanations from time series data. Once the cause is determined using these explanations, corrective measures can be implemented to prevent repeat occurrences of strip breakage, thereby improving production efficiency. Falkonry’s time series AI will analyze the tandem rolling mill’s operational data in real-time and provide actionable insights directly to production and maintenance teams in the steel mill.
The project aims to improve performance and productivity for steel manufacturing by
1. Delivering a Smart Manufacturing Profile of a tandem cold rolling mill
2. Developing a reusable solution to tackle strip break classification in cold rolling of steel.
3. Reducing man-hours consumed in the classification process, increasing uptime for the cold rolling mill and reducing scrap generated due to strip break events
Falkonry is an AI software provider that enables industrial companies to improve their output and quality through AI, advanced analytics, and visualization. Its time series AI suite of products can review terabytes of sensor and machine data in real-time to reveal actionable intelligence on excursions and faults.