J. Trzaska

Optimization of Heat Treatment Conditions of Magnesium Cast Alloys

In this paper there are presented results of the optimization of heat treatment conditions, which are temperature and heating time during solution heat treatment or ageing as well the cooling rate after solution treatment for MCMgAl12Zn1, MCMgAl9Zn1, MCMgAl6Zn1, MCMgAl3Zn1 cast magnesium alloys. A casting cycle of alloys has been carried out in an induction crucible furnace using a protective salt bath Flux 12 equipped with two ceramic filters at the melting temperature of 750±10°C, suitable for the manufactured material. The heat treatment involve the solution heat treatment and cooling in different cooling mediums as well water, air and furnace. The improvement of the manufacturing technique and chemical composition as well as of heat treatment and cooling methods leads to the development of a material designing process for the optimal physical and mechanical properties of a new developed alloy.

Use of Neural Networks and Artificial Intelligence Tools for Modeling, Characterization, and Forecasting in Material Engineering

This chapter presents the theoretical basis concerning the broad possibilities offered by the contemporary applications of artificial intelligence tools, especially artificial neural networks in the field of material engineering. The examples of own research pursued at the Institute of Engineering Materials and Biomaterials of the Silesian University of Technology, including the modeling and simulation of different properties of engineering materials, are presented. Discussed separately is a pioneering project of implementing artificial neural networks in order to predict the development trends of materials surface engineering.

Modified Tartagli Method for Calculation of Jominy Hardenability Curve

Basing on the experimental results of the hardenability investigations, which employed Jominy method, the model of the neural networks was developed and fully verified experimentally. The model makes it possible to obtain Jominy hardenability curves basing on the steel chemical composition. The modified hardenability curves calculation method is presented in the paper, initially developed by Tartaglia, Eldis, and Geissler, later extended by T. Inoue. The method makes use of the similarity of the Jominy curve to the hyperbolic secant function. The empirical formulae proposed by the authors make calculation of the hardenability curve possible basing on the chemical composition of the steel. However, regression coefficients characteristic for the particular steel grade must be known. Replacing some formulae by the neural network models is proposed in the paper.

Artificial Neural Networks Used for Development Prediction of State-of-the-Art Surface Engineering Areas

The paper presents new, possible applications of artificial neural networks in the field of materials science and material engineering in relation to other artificial intelligence methods known and applied in this area. The most recent simulation experiments, the exemplary results of which are presented in this paper, point out that the scope of the existing applications of artificial neural networks can be extended to encompass new areas related to prediction of development of materials treatment and processing technologies. The goal of such research is to focus, intentionally, the areas of future research and investments on the most promising areas likely to yield the highest added value in the future together with mitigating a risk relating to such a process. The computational models created were used for creating multi-variant probabilistic scenarios of future events based on heuristic independent variables acquired in the process of multi-stage expert surveys. Dependencies were determined, in particular, between the probability of occurrence of alternative macro-scenarios of future events and the development of the relevant thematic areas of M1–M7 and P1–P7.