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Cutting & Tools in Technological System
Article . 2020 . Peer-reviewed
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Cutting & Tools in Technological System
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SOFTWARE AND MATHEMATICAL COMPLEX FOR MULTICRITERIAL OPTIMIZATION OF TURNING PARAMETERS ON HEAVY MACHINES

Authors: Myronenko, Yevhenii; Mirantsov, Serhii; Guzenko, Vitalii; Guzenko, Denys;

SOFTWARE AND MATHEMATICAL COMPLEX FOR MULTICRITERIAL OPTIMIZATION OF TURNING PARAMETERS ON HEAVY MACHINES

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.

В роботі розглянуті питання створення програмно-математичного комплексу (ПМК) для багатокритеріальної оптимізації. Одним з найбільш перспективних напрямків підвищення ефективності процесів механічної обробки є застосування різальних інструментів зі зносостійкими покриттями, які знаходять усе більшого застосування для напівчистової та для чорнової токарної обробки деталей важкого машинобудування. Для підвищення ефективності токарної обробки на важких токарних верстатах розроблено програмно-математичний комплекс для багатокритеріальної оптимізації параметрів технологічного процесу й технологічної системи важких токарних верстатів. ПМК дозволяє виконувати настроювання значень цільових функцій оптимізації, параметрів створюваних нейронних мереж, генетичного алгоритму. Для виконання задачі багатокритеріальної оптимізації параметрів технологічного процесу механічної обробки в ПМК закладена можливість завдання параметрів інструмента (різальних пластин), з наступним формуванням і нагромадженням бази інструменту, завдання параметрів оброблюваної деталі: завдання матеріалу й зусиль різання. Також для різних параметрів технологічного переходу передбачена можливість сформувати таблицю нормативних параметрів, по якій буде зроблено навчання нейронної мережі.

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Keywords

технологічна система, режим різання, technological system, technological process, algorithm, штучний інтелект, багатокритеріальна оптимізація, програмно-математичний комплекс, tool, target function, neural network., cutting mode, artificial intelligence, software-mathematical complex, інструмент, цільова функція, multicriteria optimization, важкі токарні верстати, технологічний процес, алгоритм, heavy lathes, нейронна мережа., УДК 621.91.01

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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