at Google ScholarTitle data
Ma, Xiaohu ; Fischerauer, Alice ; Fischerauer, Gerhard:
Estimating bentonite content in foundry sands using machine learning from measured electrical impedance spectra.
In:
Abstract Book : International Workshop on Impedance Spectroscopy : IWIS 2024. -
Chemnitz
: Technische Universität Chemnitz
,
2024
. - pp. 32-34
Abstract in another language
Monitoring the bentonite content in molding sand is crucial for ensuring high-quality castings. This study investigates the suitability of convolutional neural networks (CNNs) for extracting bentonite content from the electrical impedance spectra of molding samples in conjunction with selected other measured properties of the materials. Using various synthetic molding sands as materials under test, a regression neural network was developed and tuned to estimate the bentonite content in the molding sands. This model achieved a coefficient of determination of 0.93.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | No |
| Keywords: | EIS; machine learning; FCNN; bentonite; foundry |
| Institutions of the University: | Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Faculties |
| Result of work at the UBT: | No |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 01 Oct 2024 09:55 |
| Last Modified: | 01 Oct 2024 09:55 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/89973 |