SOFTWARE AND MATHEMATICAL COMPLEX FOR MULTICRITERIAL OPTIMIZATION OF TURNING PARAMETERS ON HEAVY MACHINES
DOI:
https://doi.org/10.20998/2078-7405.2020.92.07Keywords:
heavy lathes, technological process, technological system, tool, cutting mode, multicriteria optimization, software-mathematical complex, algorithm, target function, artificial intelligence, neural network.Abstract
The issues of creation of a software-mathematical complex (SMC) for multicriteria optimization are considered in the work. One of the most promising ways to increase the efficiency of machining processes is the use of cutting tools with wear-resistant coatings, which are increasingly applied for semi-finishing and rough turning of heavy machinery parts. Objective: to increase the efficiency of machining processes on heavy lathes due to multi-criteria optimization of the parameters of the rough turning process and the parameters of the technological system. Object of study: the machining processes on heavy lathes for parts such as bodies of revolution weighing up to 20 tons. Subject of study: the relationship between the efficiency of rough turning operations on heavy lathes and the geometric and design parameters of the cutting tool. To increase the efficiency of turning on heavy lathes, a software and mathematical complex for multicriteria optimization of the technological process parameters and technological system of heavy lathes has been developed. SMC allows to adjust the values of the target optimization functions, as well as the parameters of generated neural networks and the genetic algorithm. To perform the task of multicriteria optimization of the parameters of the technological process of machining in SMC there is a possibility of setting the parameters of the tool (cutting plates), followed by formation and accumulation of the tool base and setting the parameters of the workpiece: specification of the material and of the cutting effort. Also for various parameters of technological transition a possibility to form the table of normative parameters is provided, by which training of a neural network will be made.References
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