Neural network modelling of flow stress in Ti–6Al–4V alloy with equiaxed and Widmanstätten microstructures

Abstract

In the present study, artificial neural networks (ANNs) were used to model flow stress in Ti-6Al-4V alloy with equiaxed and Widmanstä tten microstructures as initial microstructures. Continuous compression tests were performed on a Gleeble 3500 thermomechanical simulator over a wide range of temperatures (700-1100uC) with strain rates of 0?001-100 s 21 and true strains of 0?1-0?6. These tests have been focused on obtaining flow stress data under varying conditions of strain, strain rate, temperature, and initial microstructure to train ANN model. The feed forward neural network consisted of two hidden layers with a sigmoid activation function and backpropagation training algorithm was used. The architecture of the network includes four input parameters: strain rate : e, temperature T, true strain e and initial microstructure and one output parameter: the flow stress. The initial microstructure was considered qualitatively. The ANN model was successfully trained across (azb) to b phase regimes and across different deformation domains for both of the microstructures. Results show that the ANN model can correctly reproduce the flow stress in the sampled data and it can predict well with the nonsampled data. A graphical user interface was designed for easy use of the model.

Artificial neural network modeling on the relative importance of alloying elements and heat treatment temperature to the stability of α and β phase in titanium alloys

“…The experimental measurements coming from the experiments conducted in house [15][16][17][18] and from the literature [19][20][21][22][23][24] belonging to seventeen titanium alloys at different quenching temperatures has been used in the present study. The larger part of the data sets in the present work are the a + b alloys; and the remaining data are near-a and single phase a alloys.…”

Modeling and Analysis of the Plastic Flow Curves of a Duplex Stainless Steel Using Artificial Intelligence

“…ANN can learn from examples and recognize paths in a series of inputs and outputs data without any prior knowledge of their natures and interrelations. This method has been applied for different materials with good performance [28][29][30][31][32][33][34] . Also, one kind of artificial intelligence technique combining neural networks and the capabilities of the fuzzy logic inference system learning (ANFIS) can be used to establish accurate mapping relations between inputs and outputs data 35 .…”

Archives of Metallurgy and Materials

“…However, it is difficult to predict the tensile properties of dual-phase steels because the microstructure varies depending on alloying elements or process conditions. on the other hand, artificial neural networks (ANN) have been applied to predict various natural and social phenomena because they have many advantages in solving the complexity between the dependent and independent parameters [17][18][19][20][21][22][23]. The ANN techniques with these advantages have been increasingly used in materials science to design alloys and to predict mechanical properties.…”