Elastic constants of austenitic stainless steel: Investigation by the first-principles calculations and the artificial neural network approach

Composition Optimum Design and Strengthening and Toughening Mechanisms of New Alumina-Forming Austenitic Heat-Resistant Steels

“…All calculated mechanical properties are summarized in Figure 8a, which shows the elastic modulus of different systems. The bulk modulus B and shear modulus (G) of Fe 8 Cr 4 Ni 4 were found to be in the order of 250.40 GPa and 397.73 GPa; these values agreed to a large extent with those reported in Reference [34,48]. It was found that as different contents of Al atoms were introduced into the Fe 8 Cr 4 Ni 4 system, the bulk modulus (B), the shear modulus (G), and Young's modulus (E) all decreased slightly, demonstrating the weaker tendency of the resistant ability to deformation and the increased elasticity and compressibility in the structures.…”

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“…As a result, ANN might be properly used to keep away from experimental consider outs in addition to simulations to have discover of this formability of Ti-6Al-4V sheet. This specific research newspaper [27] details an approach used on ANN to figure out the components parameters associated with a stainless material. The experimental method of the bulge test is used to determine the components reply below loading.…”

The effects of passivation parameters on pitting potential of biomedical stainless steel

“…Three neurons in the input layer (for each of the passivation parameters), one neuron at the output layer for pitting potential, and only one hidden layer were used to define the ANN architecture [18,24]. The number of hidden neurons was selected by considering the following: i) too few neurons in the hidden layer can lead to under-fitting, i.e., inability to perform appropriate function approximation, whereas too many neurons can contribute to over-fitting [25], which results in a lack of generalization capability of the developed model; ii) the more hidden neurons, the more expressive power of the ANN -however, with the increase of the number of hidden neurons, the number of unknown parameters (weights and biases) to be estimated also increases; (ii) the upper limit of the number of hidden neurons can be determined considering that the total number of unknown parameters does not exceed the number of available data for training process.…”

“…Coefficient of determination R 2 was used to evaluate the performance of the developed models, and indicate how well mathematical models fitted experimental data [24]. In addition, for the estimation of the prediction performance of the developed mathematical models, relative error, as one of the most stringent criteria, was calculated by using the following equation:…”