DOI: https://doi.org/10.15588/1607-3274-2018-2-6

APPLICATION OF ACOUSTIC ANALYSIS IN CONTROL SYSTEMS OF ROBOTIC MACHINE TOOLS

О. S. Kovalevska, S. V. Kovalevskyy

Abstract


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.

Keywords


acoustic diagnostics; robotic machine tool; neural networks;reference model.

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