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To create a mechatron model of vacuum tube vibration system for rock cutting by flat auger tools based on physical and mechanical properties of processed array and kinematic characteristics of the instrument. The analytical model of the fracture process of rock cutting by tool vibration with considering of plastic properties of the massif were developed. The main technological parameters of massif vibrating cutting with normal variations were modeled: dependences of normal and tangential pressure in the zone of working body interaction with the medium, normal and shear stresses in the zone of destruction, rock characteristics of the medium, vibration parameters dependences on the characteristics of mechatronic system geometry of the contact area of flat incisors treated with medium. The choice of the computational model of the vibration rock cutting with the normal to the direction of movement of the working body fluctuations with considering arising from processes at once or disorders: the occurrence of compressive and tensile stresses were backgrounded. Main stages and interconnection options in the simulation of vibration cutting were established. Scientific novelty lies in the development of a method of analysis of contact interaction of roller working body molding machine with an array taking into account the changes in the stabilization process of physical and mechanical properties of the treated medium, the aim of which is to predict the required voltage and depth of the formed layer. The theoretical basis of rock cutting by flat auger tool taking into account the normal component of the vibration with respect to the movement direction allowing for the deformation of the rock mass and the contact interaction with the working body were established, that allows to improve the technology of drilling wells by reducing the power consumption of the cutting process. The results enable us to determine the parameters of power and kinematic conditions for the occurrence of the vibration cutting.

This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.