Online prediction and monitoring of mechanical properties of industrial galvanised steel coils using neural networks

ABSTRACT

In galvanising line of cold rolling mill, mechanical properties, i.e. yield strength (YS) and ultimate tensile strength (UTS), are achieved by controlling the key process parameters within specified limits. In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to predict the mechanical properties of a coil from its chemical composition, thickness, width and key galvanising process parameters. Principal component analysis is used to avoid redundancy and collinearity effects in input variables for the ANN. The model predicted the YS and UTS with an accuracy of ±10 megapascal (MPa) for 90% of the data. The model was implemented in the continuous galvanising line of Tata Steel, India. An online quality monitoring system was developed to monitor the predicted mechanical properties and process parameters of a galvanised coil. This system helps quality team in decision making.

KEYWORDS:

• Galvanising
• artificial neural networks
• mechanical properties
• principal component analysis
• online quality monitoring
• prediction
• yield strength
• ultimate tensile strength

Acknowledgement

The authors acknowledge the help extended by Mr Rajesh Shyam Pais of Tata Steel for the selection of input process parameters of the neural network. They thank the management of Tata Steel for their kind permission.

Disclosure statement

No potential conflict of interest was reported by the authors.