Transactions of the Indian Institute of Metals

, Volume 72, Issue 12, pp 3015–3025 | Cite as

Neural Network Modelling to Characterize Steel Continuous Casting Process Parameters and Prediction of Casting Defects

  • Sheuli HoreEmail author
  • Suchandan K. Das
  • Manoj M. Humane
  • Anil Kumar Peethala
Technical Paper


The current work outlines application of a data-driven multilayer perceptron-based artificial neural network (ANN) model to characterize the influence of melt compositions, tundish temperature, tundish superheat, casting speed and mould oscillation frequency on the important processing parameters such as mould powder consumption rate, oscillation mark depth and metallurgical length during continuous casting process. A two-layer feedforward back-propagation neural network model has been developed for predicting the probability of occurrence of defect in the cast product. The network training architecture has been optimized using a gradient-based algorithm, namely the back-propagation algorithm. The neural network predictions are found to be in good agreement with regard to oscillation mark depth, mould powder consumption rate, metallurgical length and probability of occurrence of defect using data obtained from an operating Indian steel plant (Rashtriya Ispat Nigam Limited, Visakhapatnam). The ANN model prediction has been validated successfully with multiple linear regression analysis carried out on each data set.


Artificial neural network Continuous casting Casting defects Metallurgical length Mould oscillation parameters Oscillation mark depth 



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Copyright information

© The Indian Institute of Metals - IIM 2019

Authors and Affiliations

  1. 1.CSIR-National Metallurgical LaboratoryJamshedpurIndia
  2. 2.Visakhapatnam Steel Plant (RINL)VisakhapatnamIndia

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