In the paper, the authors present the approach to modelling of austenitic steel hardening basing on the Frederick-Armstrong’s rule and Chaboche elastic-plastic material model with mixed hardening. Non-linear uniaxial constitutive equations are derived from more general relations with the assumption of an appropriate evolution of back stress. The aim of the paper is to propose a robust and efficient identification method of a well known material model. A typical LCF strain-controlled test was conducted for selected amplitudes of total strain. Continuous measurements of instant stress and total strain values were performed. Life time of a specimen, signals amplitudes and load frequency were also recorded. Based on the measurement, identification of constitutive equation parameters was performed. The goal was to obtain a model that describes, including hardening phenomenon, a material behaviour during the experiment until the material failure. As a criterion of optimisation of the model least square projection accuracy of the material response was selected. Several optimisation methods were examined. Finally, the differential evolution method was selected as the most efficient one. The method was compared to standard optimisation methods available in the MATLAB environment. Significant decrease of computation time was achieved as all the optimisation procedures were run parallel on a computer cluster.

In the paper the problem of optimum experimental design for estimating parameters of multivariate regression functions is considered. We address the question: under what conditions one can compose the optimal design from partial designs, obtained by considering partial regressions, which depend on reduced number of variables. After reinterpreting and reviewing briefly existing results we provide some new conditions

The book is a guide to the software of the Experiment Operation System (EOS) implemented under the ministry program RR I.14, and the experiment support system software. It may also be helpful in expanding your own Service Systems or in creating your own versions of the Computer Aided Experiment (CAE) software. The book is intended for people involved in experimental research who do not specialize in computer science and statistical data processing. Substantive descriptions of algorithms may be of interest to specialists in particular fields. (IN POLISH)

In the note Hoel’s result (1965, Ann. Math. Statist., 36, 1097- 1106) is generalized to a large family of experimental design optimality criterions. Sufficient conditions for optimality criterion are given, which ensure existence of the optimum experimental design measure which is a product of design measures on lower dimensional domains.