| About this Abstract |
| 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 |