The paper presents the results of a series of numerical research on the possibility of applying Artificial Neural Networks (ANNs) for the ultimate strength calculations of selected parts of rotating machines. The layout and the principle of the algorithm operation were described, beginning from the general assumptions and then moving on to the detailed description of the subsequent modules. The effects of applying the algorithm were presented on the example of the compressor disc analysis. The significant benefits of its application were the reduction of optimization time by about 40% and the disc weight reduction by 0.5 kg. The accuracy of ANNs was different in each iteration of the presented algorithm. Finally, high accuracy of neural networks was achieved with the following mean values of relevant indices reached in the last iteration: RMSE = 0.5983, MAPA = 0.0733 and R 2 = 0.99895. The further perspectives of the undertaken research were defined at the end.

The article presents the process of selecting and optimising artificial neural networks based on the example of determining the stress distribution in a disk-drum structure compressor stage of an aircraft turbine engine. The presented algorithm allows the determination of von Mises stress values which can be part of the penalty function for further mass optimization of the structure. A method of a parametric model description of a compressor stage is presented in order to prepare a reduced stress distribution for training artificial neural networks. A comparative analysis of selected neural network training algorithms combined with the optimisation of their structure is presented. A genetic algorithm was used to determine the optimal number of hidden layers and neurons in a layer. The objective function was to minimise the absolute value of the relative error and standard deviation of stresses determined by FEM and artificial neural networks. The results are presented in the form of the Pareto front due to the stochastic optimisation process.

In this paper authors show results of optimization of compressor discs in turbine engines. The problem of optimizing the thickness of the disc brought to the NP-complete problem, and solved it by using one of the genetic algorithms – evolutionary algorithm. Correctness of model and optimization algorithm were constantly checked. At the end of this paper, compressor disc created due to traditional technology and disc created by BLISK technology were compared.

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.