jsen_im_submit.pdf (2.89 MB)
Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach
preprint
posted on 2021-03-12, 02:37 authored by Imamul MuttakinImamul Muttakin, Manuchehr SoleimaniIdentification of gas
bubble, void detection and porosity estimation are important factors in many
liquid metal processes. In steel casting, the importance of flow condition and
phase distribution in crucial parts, such as submerged entry nozzle (SEN) and
mould raises the needs to observe the phenomena. Cross-section of flow shapes
can be visualised using the magnetic induction tomography (MIT) technique.
However, the inversion procedure in the image reconstruction has either limited
resolution or complex computation degrading its real-time capability.
Additionally, in some cases, the actual image may not be essential whereas the
void fraction or porosity needs to be estimated. This work proposes an interior
void classifier based on multi-frequency mutual induction measurements with
eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed
plastic miniature of an SEN. The sensors consist of eight coils arranged in a
circle encapsulating the column, providing combinatorial detection on
conductive surface and depth. The datasets are induced voltage collections of
several non-metallic inclusions (NMI) patterns in liquid metal static test and
used to train a machine learning model. The model architectures are a fully
connected neural network (FCNN) for 1D; and a convolutional neural network
(CNN) for 2D data. The classifier using 1D data has been trained to
approximately 86% accuracy on this dataset. CNN classification using
multi-dimensional data with more classes produces 96% of test accuracy. Refined
with representative flow scenarios, the trained model could be deployed for an
intelligent online control system of the liquid metal process.
Funding
H2020-EU No 764902
History
Email Address of Submitting Author
muttakin.imamul@gmail.comORCID of Submitting Author
0000-0002-8409-4942Submitting Author's Institution
University of BathSubmitting Author's Country
- United Kingdom