The article considers methods of non-destructive testing based on various physical laws and phenomena. The possibility of creating a new topical tool for obtaining a wide range of data of mechanical engineering products such as shape, size and location in space is considered. It is proposed to use sound diagnostics using a high-frequency broadband signal to capture the frequency characteristics of the object. The purpose of the study is to develop a method of non-contact measurement of mechanical engineering products on several grounds. With the help of vibroacoustic diagnostics and the method of quantitative control, the distribution of the entire volume of products was 100 pieces. on two parties: the main and control, quantitative parameters of each unit of a product are removed. A signal from 0 to 20,000 Hz was applied by means of a frequency generator. The frequency response of each sample was recorded in the Spectrum Analiyser program. Estimation of the deviation of the product size and its frequency spectrum was performed in the NeuroPro 0.25 software. The created neural network allows is predicted in real time values of several quantitative signs irrespective of their nature. A working model for collecting statistical data for the efficient operation of the neural network is obtained. The developed technique allows detecting the configuration of products on the basis of indirect measurements through the frequency spectrum. This technique can be used to diagnose parts by geometric features, physical properties, defects. This requires an increase in input data for neural network training. With a sufficient selection of parts with different defects of the neural network on the acoustic frequency characteristics will be able to divide the parts into groups of worthy and unworthy on various grounds.
Abstract:The article contains the new materials reflecting application of neural network models in designing of innovative processes in pedagogic. Such approach, according to authors' point of view, is actual because it is very important to provide quantitative estimations along with quality standards for management of pedagogical processes. They allow to reveal tendencies of innovative pedagogical approaches in education of new generation of young men, but also to correspond to their aspirations, supporting the positive of their socioeconomic influence in every possible way.
The monograph examines the prerequisites and scientific foundations for creation of the Strategy for Artificial Intelligence Development in Ukraine as well as means and ways of its effective implementation. For specialists, postgraduate, and graduate students in the field of artificial intelligence, information technologies, philosophy, state formation, and economics
Relevance. The problem of controlling complex technological machines such as machines with mechanisms based on kinematics with parallel structure is given consideration in the article in order to improve accuracy of positioning of actuators, to ensure their dynamic adjustment and optimization of trajectories of displacement of operating elements of the equipment (cutting tools, assembling or controlling instruments). The object of the study is the model of the operating area of a mobile robotic machine tool. Objective. The goal of the work is to create a concept for controlling a mobile robotic machine tool applying acoustic control on the basis of a reference model based on deep neural networks. Method. A method of identification and control of a mobile robotic machine tool using spectral description of absorption of acoustic wave with further processing of obtained information is offered. This method allows determining accuracy of positioning of actuators, as well as conducting dynamic adjustment and optimization of trajectories of displacement of operating elements of the equipment. A method of acoustic analysis for precision machining on machine tools with parallel kinematics has been developed. Results. A neural network reference model has been constructed, which allows to diagnose current characteristics of the state of objects in different conditions, namely mechanism's configuration, mechanism's geometric parameters while running motor-spindle, dynamics of displacement of mechanism's nodes of the experimental stand with variable velocities and load on the drive, as well as temperature changes of the object. The developed neural network models also were tested for adequacy. Conclusions. The experiments on the study of the dependency between the parameters of the spectrum of the acoustic signal with a given discreteness disturbed by excitatory effect in the form of "white noise" confirmed efficiency of this approach. Prospects for further research may consist in creation of methods for optimal control of complex technological machines to improve accuracy of positioning of actuators and to improve their dynamic settings.
The article contains the new materials reflecting application of neural network models in designing of innovative processes in pedagogic. Such approach, according to authors' point of view, is actual because it is very important to provide quantitative estimations along with quality standards for management of pedagogical processes. They allow to reveal tendencies of innovative pedagogical approaches in education of new generation of young men, but also to correspond to their aspirations, supporting the positive of their socioeconomic influence in every possible way.
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