MCDM towards knowledge incorporation in ANN models for phase transformation in continuous cooling of steel

Abstract

Purpose

The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Design/methodology/approach

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

Findings

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

Originality/value

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

Citation

Chakraborty, S., Das, P., Kaveti, N.K., Chattopadhyay, P.P. and Datta, S. (2019), "MCDM towards knowledge incorporation in ANN models for phase transformation in continuous cooling of steel", Multidiscipline Modeling in Materials and Structures, Vol. 15 No. 1, pp. 170-186. https://doi.org/10.1108/MMMS-01-2018-0002