AUTOMATED DETERMINATION OF ENERGY-EFFICIENT TURNING MODES ON HEAVY MACHINES BASED ON THE RESULTS OF ANALYSIS OF CUTTING PROCESS DIAGNOSTIC DATA

Abstract

The article substantiates the principal approaches to solving the problem of automated determination of energyefficient turning modes on heavy machines based on the results of the analysis of data on the cutting process obtained during
its diagnosis. A general sequence for determining energy-efficient turning modes is presented, which provides for an automated
calculation of modes based on dependencies built on the basis of the criterion of the minimum specific energy intensity of
cutting, analysis of processing data obtained in the form of an array of diagnostic signals about the real cutting process and
subsequent correction of the calculated dependencies based on the results of diagnostic data analysis. In order to solve the
problem under consideration, a self-learning system for automated determination of energy-efficient turning modes is proposed.
The principle of operation of the self-learning system has been formulated, and its functional scheme has been developed. The
expected effect of the introduction of a self-learning system is formulated.