Interior Void Classification in Liquid Metal using Multi-Frequency Magnetic Induction Tomography with a Machine Learning Approach

Imamul Muttakin, Manuchehr Soleimani

Research output: Contribution to journalArticlepeer-review

8 Citations (SciVal)
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Identification 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 involving post-processing stages degrading its real-time capability. Additionally, when quantifying the void fraction or porosity, the image may not be required. 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 provide 95% accuracy on this dataset. On the other hand, CNN classification using multi-dimensional data 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.
Original languageEnglish
Pages (from-to)23289-23296
Number of pages8
JournalIEEE Sensors Journal
Issue number20
Early online date1 Sept 2021
Publication statusPublished - 15 Oct 2021


  • Classification
  • Liquid metal
  • Machine learning
  • Magnetic induction tomography
  • Non-metallic inclusion

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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