The article examines modern informational approaches to assess the degree of damageability of materials based on their fractographic images. The possibility of using the fractal dimension, wavelet transform and convolutional artificial neural networks for tiling and classifying the share of viscous and brittle destructions on fractures is shown. The results of experimental studies of the impact viscosity of materials with different types of crystal lattices in a wide range of temperatures are presented.

The article is devoted to the study of the evolution of the microstructure of steel 12Kh18N10T in the zone of intense deformation during fatigue loading. An algorithm for quantitative analysis of the microstructure of a material based on an indicator of fractal dimension is described. The studies have led to the conclusion that the fractal dimension of the microstructure of the material can serve as an indicator of its damage in a wide temperature range, and used as a quantitative criterion for the prefracture of the material, both during laboratory tests and during operation.

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This paper presents an analysis of the development of adaptive control systems for CNC machines. It is shown that the construction of systems for optimal control of machining processes is based on such approaches as artificial intelligence, genetic algorithms for optimizing processing modes, expert systems for knowledge accumulation, cloud technologies and the development of digital twins of the equipment. An adaptive system of intelligent control of a CNC machine is developed based on training of a neural network model, which can improve the quality of machining parts and reduce the wear of the cutting tool.