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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Digitalization for Advanced Manufacturing through Simulation, Visualization and Machine Learning
Author(s) Chenn Q. Zhou, John Moreland, Armin Silaen, Tyamo Okosun, Nicholas Walla, Kyle Toth
On-Site Speaker (Planned) Chenn Q. Zhou
Abstract Scope Computer simulation, visualization and machine learning are increasingly playing key roles in the Digitalization for advanced manufacturing. These technologies can be used to create cutting-edge physics-based and data-driven tools for real-time decision making to address critical issues related to energy efficiency, carbon-footprint and other pollutant emissions, productivity, quality, operation efficiency, maintenance, and more. These technologies can provide fundamental understating and practical guidance for process design, troubleshooting, and optimization, new process development and scale up, as well as workforce development. The Center for Innovation and Visualization through Simulation (CIVS) at Purdue University Northwest has used these technologies to develop and implement digitalization for Advanced Manufacturing in partnerships with steel and other industries. Methodologies and project examples will be presented in this paper.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Iron and Steel, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AI/Data Mining in Materials Manufacturing
Audio Signal Processing for Quantitative Moulding Material Regeneration
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace
Determining the Bubble Dynamics of a Top Submerged Lance Smelter
Development of Virtual Die Casting Simulator for Workforce Development
Digitalization for Advanced Manufacturing through Simulation, Visualization and Machine Learning
Digitalizing the Circular Economy (CE): From Reactor Simulation to System Models of the CE
Evolution of Process Models to Digital Twins
Factors to Consider when Designing Aluminium Alloys for Increased Scrap Usage
NOW ON-DEMAND ONLY - An Automated Recycling Process of End-of-life Lithium-ion Batteries Enhanced by Online Sensing and Machine Learning Techniques
Refractory Lifetime Prediction in Industrial Processes with Artificial Intelligence
Steel Production Efficiency Improvements by Digitalization

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