Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Falkonry and ArcelorMittal selected for a Joint Smart Manufacturing Project by CESMII

The project will leverage Falkonry’s time series AI to automate strip break classification and reduce wastage in steel cold rolling

Falkonry, a leader in time series AI, announced it 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.

Combining Falkonry’s expertise in AI and analytics and our steel manufacturing domain experience, we are confident of developing a solution that will help reduce downtimes and other operational issues resulting from strip breaks.

“CESMII’s objective is to drive innovation through the democratization of smart manufacturing and we chose Falkonry and ArcelorMittal from a large pool of proposals to further this objective,” said John Dyck, CEO, CESMII. “Staying competitive in metal manufacturing requires reliable and efficient operations and this project aims to develop a reusable and interoperable solution for productivity improvement in steel mills.”

Recommended AI News: Employee Enablement Platform Zavvy Launches With $4 Million In Seed Funding

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 (ML), 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.

Related Posts
1 of 32,116

“This joint opportunity with Falkonry will enable us to leverage the power of AI/ML in addressing a major challenge during the cold rolling of steel in a Continuous Cold Mill – occurrence of strip breaks which result in line stoppages and pose a risk of equipment damage,” said Bernard Chukwulebe, Group Manager, Processing and Control, and Digital Transformation Cluster Leader, ArcelorMittal Global R&D East Chicago Center. “Combining Falkonry’s expertise in AI and analytics and our steel manufacturing domain experience, we are confident of developing a solution that will help reduce downtimes and other operational issues resulting from strip breaks.” According to Thomas Brennan, CRM Process Technology Area Manager at AM/NS Calvert, “High-speed strip breaks are the most damaging for the mill and Falkonry AI will especially be beneficial in providing early warning that can help us activate our strip break responses in time to reduce our yield losses.”

Recommended AI News: Wiliot Names Former Amazon Supply Chain Executive Manish Bansal as Chief Product Officer

The project aims to improve performance and productivity for steel manufacturing by:

  • Delivering a Smart Manufacturing Profile of a tandem cold rolling mill.
  • Developing a reusable solution to tackle strip break classification in cold rolling of steel.
  • Reducing man-hours consumed in the classification process, increasing uptime for the cold rolling mill and reducing scrap generated due to strip break events.

“We are delighted to have been selected by CESMII for RFP3 and look forward to working with ArcelorMittal to illustrate how control systems data can be effectively combined with time series AI to enhance operational excellence for steel manufacturers,” said Dr. Nikunj Mehta, Founder and CEO, Falkonry. “This project will leverage AI/ML to classify damaging events from control systems data and more quickly understand their causes, and deliver a scalable solution that can be readily applied across steel mills.”

Recommended AI News: GuideWell and Olive Debut First-Of-Its-Kind AI Solution to Accelerate Access to Health Care

[To share your insights with us, please write to sghosh@martechseries.com]

Comments are closed.