Determination of the bentonite content in molding sands using AI-enhanced electrical impedance spectroscopy

Title data

Ma, Xiaohu ; Fischerauer, Alice ; Haacke, Sebastian ; Fischerauer, Gerhard:
Determination of the bentonite content in molding sands using AI-enhanced electrical impedance spectroscopy.
In: Sensors. Vol. 24 (2024) Issue 24 . - 8111.
ISSN 1424-8220
DOI: https://doi.org/10.3390/s24248111

Project information

Abstract in another language

Molding sand mixtures in the foundry industry are typically composed of fresh and reclaimed sands, water and additives such as bentonite. Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution. To establish a fundamental dataset, we characterized various sand mixtures containing quartz sand, bentonite, and deionized water using EIS in the frequency range from 20 Hz to 1 MHz under laboratory conditions and also measured the water content and density of samples. Principal component analysis was applied to the EIS data to extract relevant features as input data to machine-learning models. These features, combined with water content and density, were used to train regression models based on fully connected neural networks to estimate the bentonite content in mixtures. This led to a high prediction accuracy (R2=0.94). These results demonstrate that AI-enhanced EIS has promising potential for in-line monitoring of bulk material in the foundry industry, paving the way for optimized process control and efficient sand recycling.