Journal of Advanced Research

Journal of Advanced Research

Volume 18, July 2019, Pages 173-184
Journal of Advanced Research

Original article
A regression-tree multilayer-perceptron hybrid strategy for the prediction of ore crushing-plate lifetimes

https://doi.org/10.1016/j.jare.2019.03.008Get rights and content
Under a Creative Commons license
open access

Highlights

  • Dataset of plates lifetime were obtained by 3 casting methods and chemical composition.

  • A two-steps model for prediction of the full lifetime of plates of Hadfield steel was proposed.

  • The prediction model combines regression trees with multilayer perceptron (MLP)

  • MLP provides accurate weaŕs models considering the chemical composition.

  • Regression trees provide visual information about dataset structure to build MLP.

Abstract

Highly tensile manganese steel is in great demand owing to its high tensile strength under shock loads. All workpieces are produced through casting, because it is highly difficult to machine. The probabilistic aspects of its casting, its variable composition, and the different casting techniques must all be considered for the optimisation of its mechanical properties. A hybrid strategy is therefore proposed which combines decision trees and artificial neural networks (ANNs) for accurate and reliable prediction models for ore crushing plate lifetimes. The strategic blend of these two high-accuracy prediction models is used to generate simple decision trees which can reveal the main dataset features, thereby facilitating decision-making. Following a complexity analysis of a dataset with 450 different plates, the best model consisted of 9 different multilayer perceptrons, the inputs of which were only the Fe and Mn plate compositions. The model recorded a low root mean square error (RMSE) of only 0.0614 h for the lifetime of the plate: a very accurate result considering their varied lifetimes of between 746 and 6902 h in the dataset. Finally, the use of these models under real industrial conditions is presented in a heat map, namely a 2D representation of the main manufacturing process inputs with a colour scale which shows the predicted output, i.e. the expected lifetime of the manufactured plates. Thus, the hybrid strategy extracts core training dataset information in high-accuracy prediction models. This novel strategy merges the different capabilities of two families of machine-learning algorithms. It provides a high-accuracy industrial tool for the prediction of the full lifetime of highly tensile manganese steel plates. The results yielded a precision prediction of (RMSE of 0.061 h) for the full lifetime of (light, medium, and heavy) crusher plates manufactured with the three (experimental, classic, and highly efficient (new)) casting methods.

Keywords

Hadfield steel
Resource savings
Lifetime prediction
Regression trees
Multi-layer perceptrons
Artificial intelligence

Cited by (0)

Peer review under responsibility of Cairo University.