How AI Is Increasing Production Quality in Steel Manufacturing

John Devins and Venkatesh Muthusamy, Canvass Analytic

Wednesday, 24 June 2020
10-11 a.m.

►Register Now | ►Add to Calendar  | ►Sponsor this Webinar

This presentation will outline a case study of how a major North American steel producer is using AI-powered industrial analytics to improve yield reduce costs and production delays and improve reliability. The discussion will look at the journey the manufacturer took from transforming its manual quality control processes by implementing AI industrial analytics. AI-powered industrial analytics is helping the producer to identify and control the parameters that impact quality and help the operations teams to control their production processes by implementing near-real-time adjustments required to improve production and batch consistency. As a result of using industrial analytics to gain real-time visibility into production quality and control the quality control processes, the company improved production while reducing production costs and opening new markets by producing higher-grade products so much so that the manufacturer now applies AI to its day-to-day operations across their steelmaking, hot strip mill and cold mill areas.
 
John Devins
Bio- John Devins is vice president, customer success at Canvass Analytics, an artificial intelligence (AI) industrial analytics software company that helps process engineers and operators to improve complex operational processes and optimize assets by using AI and machine learning. In his role, Devins is responsible for the successful delivery of machine learning projects, accelerating adoption by Canvass users and fostering collaboration. Prior to joining Canvass, Devins held global business development and sales operation executive roles spanning APAC, Europe and the Middle East and Africa.
 
Venkatesh Muthusamy
Bio- Venkatesh Muthusamy is a senior data scientist at Canvass Analytics, where he is part of the team that builds, tests and operationalizes machine learning models for Canvass clients. With experience spanning the steel and auto manufacturing, energy, and aerospace sectors, Muthusamy’s industry expertise helps bridge the divide between industrial process engineers and data science and machine learning. Muthusamy has a master of aerospace, aeronautical and astronautical/space engineering degree from Ryerson University.

Moderator:

Sponsored By:

Organized By:
AIST’s Electrical Applications/Sensors Subcommittee and Digitalization Applications Technology Committees.