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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title AI/Data Mining in Materials Manufacturing
Author(s) Elsa Olivetti
On-Site Speaker (Planned) Elsa Olivetti
Abstract Scope Presently commercialized technologies, silicon photovoltaics and lithium-ion batteries, have required decades to reach even modest levels of global adoption. One among many factors contributing to this protracted transition to large scale manufacturing is that the processes and equipment used in lab diverge from those which are available or required at production scales. When novel materials or devices proceed from the laboratory to manufacturing, unforeseen and unfamiliar processing challenges can easily arise. Fundamental scientific and design problems that could be solved during early-stage research with comparatively little time and expense must instead be addressed during scale up, compelling the use of significantly more capital at a time when such delays are ill-afforded and in an industry in which low-cost incumbents drive perilously thin margins for new entrants. This presentation will focus on data-driven insight and tools for early research to design new technologies explicitly for manufacturing and scale up from inception.
Proceedings Inclusion? Planned:


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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