Flow curve prediction of cold forging steel by artificial neural network model

Abstract

A limited number of material models or flow curves are available in commercial finite element softwares at varying temperature and strain rate ranges for plasticity analysis. To obtain more realistic finite element results, flow curves at wide temperature and strain rate ranges are required. For this purpose, a material model for a medium carbon alloy steel material which is used for fastener production was prepared. Firstly, flow curves of the material were obtained at 4 temperatures (20, 100, 200, 400 °C) and 3 strain rates (1, 10, 50 s-1). Then, experimental data was used to construct an artificial neural networks model (ANN) for the material. 75% of the experimental data was used to train the model and the rest was employed for validation and verification. ANN model used in flow curve prediction was developed using the scikit-learn library on Python. Temperature, strain rate and strain were employed as input parameters and flow stress as output parameter in ANN model. In order to increase the accuracy of the ANN model, the number of hidden layers and the number of neurons were also optimized by mean squared error approach. As a result of studies, an ANN-based material model that can be used for wide range of temperature and strain rate values were developed based on the experimental data.

Combined material model to predict flow curves of cold forging raw materials having high strain hardening exponent

“…The combination of Swift model and the 4th order polynomial was also proposed in order to describe the large deformation behavior of the Ti-6Al-4V alloy in the same study. In addition to predicting material flow curves with mathematical models, there are also studies carried out to predict flow curves with machine learning or regression models [10,11]. An artificial neural network model was suggested by Kocatürk et al [10] to estimate experimental flow curves of a medium carbon steel material at different temperatures and strain rate values, and the flow curve predictions with high accuracy were obtained with this method.…”

“…In addition to predicting material flow curves with mathematical models, there are also studies carried out to predict flow curves with machine learning or regression models [10,11]. An artificial neural network model was suggested by Kocatürk et al [10] to estimate experimental flow curves of a medium carbon steel material at different temperatures and strain rate values, and the flow curve predictions with high accuracy were obtained with this method. In another study, Aydın et al [11] proposed a model that can obtain true stress-strain curve from experimental compression test data.…”

A Model to Construct and Predict Flow Curve of Materials from Compression Test Results with Machine Learning Models Using Python

“…Kabliman et al (2019) [9] preferred machine learning and estimated stress-strain curves for aluminium alloys using symbolic regression. Lastly, Kocatürk et al (2021) [10] obtained flow curves at different temperature and strain rate values for the medium carbon steel alloy frequently used in fastener production. Flow curves were predicted using ANN for intermediate temperature and intermediate strain rate values with experimentally obtained flow curves.…”