Digitalization for Advanced Manufacturing Through Simulation, Visualization, and Machine Learning

Digitalization for Advanced Manufacturing Through Simulation, Visualization, and Machine Learning

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Bibliographische Detailangaben
Veröffentlicht in:REWAS (2022 : Anaheim, Calif.) REWAS 2022 ; Volume 1: Developing tomorrow's technical cycles
1. Verfasser: Zhou, Chenn (VerfasserIn)
Weitere Verfasser: Moreland, John (VerfasserIn), Silaen, Armin (VerfasserIn), Okosun, Tyamo (VerfasserIn), Walla, Nick (VerfasserIn), Toth, Kyle (VerfasserIn)
Pages:2022
Format: UnknownFormat
Sprache:eng
Veröffentlicht: 2022
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Recycling Technologies Developed at KIGAM 2022 Lee, Jin-Young
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Sustainability-Based Selection of Materials for Refractory High Entropy Alloys 2022 Wang, Xinyi
Economics-Informed Material System Modeling of the Copper Supply Chain 2022 Ryter, John
Environmental Benefits of Closing the Solar Manufacturing and Recycling Loop: Preparation of Solar Manufacturing Inventories 2022 Flores, Robert
Digitalizing the Circular Economy (CE): From Reactor Simulation to System Models of the CE 2022 Reuter, Markus A.
Factors to Consider When Designing Aluminium Alloys for Increased Scrap Usage 2022 Montanelli, Luca
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace 2022 Rao, Vivek M.
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Recovery of Copper, Iron, and Alumina from Metallurgical Waste by Use of Hydrogen 2022 Eijk, Casper van der
Recovery of Rare Earth Elements from Nd-Fe-B Magnet Through Selective Chlorination Using Zinc Chloride 2022 Lim, Kyung-Hwan
Electrochemical Pathways Towards Sustainable Energy 2022 Sadoway, Donald R.
Actions of the Copper Industry Toward a Future Carbon-Neutral Society 2022 Nakamura, Takashi
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