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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium REWAS 2022: Automation and Digitalization for Advanced Manufacturing
Presentation Title Audio Signal Processing for Quantitative Moulding Material Regeneration
Author(s) Philine Kerst, Sebastian Tewes
On-Site Speaker (Planned) Philine Kerst
Abstract Scope As a natural product with limited resources, sand is of existential importance for various industries. Foundry technology in particular requires considerable quantities worldwide for sand moulds and cores. Depending on the process, the sand is recycled and thus reused. However, especially in the case of inorganically bound moulding sand, it is still frequently not recycled. Existing regeneration methods include mechanical, pneumatic, or combined processes. These have been developed, but have reached their analytical optimisation limits, as the processes are not transparent and make insitu analyses of moulding materials impossible. Within the scope of this research work, a methodology for the computer-aided processing of sound and image data was developed with the help of Convolutional Neural Networks (CNN), which is to evaluate the non-measurable changes of the moulding material in the running process in real time via process acoustics. The aim is to optimise process control in terms of time, cost, and energy efficiency.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Process Technology, Other


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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