Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

Abstract

In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an Auto-Encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response – especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance.