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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Development of Virtual Die Casting Simulator for Workforce Development
Author(s) John Moreland, Kyle Toth, John Estrada, Junyi Chen, Na Zhu, Chenn Q. Zhou
On-Site Speaker (Planned) Chenn Q. Zhou
Abstract Scope High pressure die casting is a complex manufacturing process that requires a highly developed work force. A virtual die casting machine has been developed for operators to provide a better understanding of how the machine works and how to deal with a variety of practical situations and issues that arise on the shop floor. Computational fluid dynamics (CFD) simulations have also been developed and integrated into the simulator to help die casters understand how parameters such as shot speed can affect the resulting quality of castings being produced. A virtual melter furnace is also being developed to learn and practice maintenance and safety procedures. The simulator was developed for virtual reality (VR) headsets and controllers, but is also usable on standard PC with mouse and keyboard. Development methodology and overview of simulator functionality will be discussed.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Other, Aluminum

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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