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

Abstract

In order to obtain flow curves from compression test results of a cold forging material and predict flow curves of the material at intermediate temperature and strain rate values, a model was developed using Python programming language in this study. The model consists of two parts: Flow curve determination and flow curve prediction. The compression test data including Force-Stroke values was processed to determine the flow curves in the first part, and the flow curve data constructed for certain temperature and strain rate values of the material was used as input for the machine learning algorithms to predict flow curve at desired intermediate temperature and strain rate values in the second part. Moreover, Ludwik material model parameters were estimated by using curve fitting methods in order to define the material model into the simulation software. Machine learning algorithms and various regression models in Python libraries were tested to predict the flow curves. The performances of different machine learning and regression models were compared with respect to the mean squared error and coefficient of determination performance measures. Support vector regression, k-Nearest Neighbour (kNN) and artificial neural network models were used to predict flow curves of cold forging materials and kNN regression model was able to found predictions with the lowest error rate. As a result, a model that can process the compression test data to predict flow curves at intermediate temperature or strain rate values was developed.

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

“…The flow curves were obtained from the experimental compression test results of 42CrMoS4 medium carbon alloy steel material. To obtain the flow curves from the experimental compression test data of the materials, the model suggested by Aydın et al [11] was used. Compression tests were carried out at different temperatures and strain rates in accordance with ASTM E9 standard [12].…”

“…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.…”

“…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. Moreover, machine learning models and various regression models were used to predict the flow curves for the intermediate temperature and strain rate values where experimental flow curves are not available and promising flow curves were estimated.…”