Development of ANN model and study the effect of temperature on strain ratio and sensitivity index of EDD steel

Abstract

Anisotropy and strain rate sensitivity index (m) plays a very important role in the formability of materials. In the present investigation strain ratios in 0°, 45°and 90°t o the rolling direction and the strain rate sensitivity index were calculated at different temperatures. After developing the data from experiments, Artificial Neural Network (ANN) models are trained for different properties. Trained ANN models are used to calculate different strain ratios and sensitivity index at unknown temperatures. The results are promising and the percentage error in ANN prediction is found to be around 10%.

Application of support vector regression in predicting thickness strains in hydro-mechanical deep drawing and comparison with ANN and FEM

“…But it was also found that certain materials there will be brittle fracture due to the appearance of blue brittle regime [6]. ANN models were applied by Singh et al [6] and Singh [7] to predict the material properties at unknown temperature to understand the formability of material. So, in the present investigation a SVR model is being developed based on the experimental results of Andrsen [8] and Singh and In this paper, a new data mining technique support vector regression (SVR) is applied to predict the thickness along cup wall in hydro-mechanical deep drawing.…”