Abstract
The aim of this paper is an attempt to answer the question of whether, on the basis of the values of the mechanical properties of ferritic stainless steels, it is possible to predict the chemical concentration of carbon and nine of the other most common alloying elements in these steels. The author believes that the relationships between the properties are more complicated and depend on a greater number of factors, such as heat and mechanical treatment conditions, but in this paper, they were not taken into account due to the uniform treatment of the tested steels. The modeling results proved to be very promising and indicate that for some elements, this is possible with high accuracy. Artificial neural networks with radial basis functions (RBF), multilayer perceptron with one and two hidden layers (MLP) and generalized regression neural networks (GRNN) were used for modeling. In order to minimize the manufacturing cost of products, developed artificial neural networks can be used in industry. They may also simplify the selection of materials if the engineer has to correctly select chemical components and appropriate plastic and/or heat treatments of stainless steel with the necessary mechanical properties.
Highlights
Developments in material engineering have resulted in increased market competition, especially for corrosion-resistant steels
An automatic network designer was used to estimate the number of neurons in hidden layers for artificial neural networks of the radial basis functions (RBF) and Multi-layer perceptron (MLP) type
The greatest efficiency in modeling the chemical composition of ferritic stainless steels shown by general regression neural networks (GRNN)
Summary
Developments in material engineering have resulted in increased market competition, especially for corrosion-resistant steels. These materials’ properties are strictly dependent on their chemical composition and processing type. The classical approach, i.e., the execution of a series of experiments with the development of the required number of samples to determine the characteristics of each of these steel grades, is a breakneck undertaking that requires an extremely large amount of time and financial expenditure. Artificial intelligence techniques, together with experimental data, enable the creation of a model that enables the chemical composition of stain-ferritic steels to be predicted with high precision in a very short time. The use of artificial intelligence allows stainless steel technology to be advanced in many respects, even though only a limited number of definition vectors are available [1–12]
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