z-logo
open-access-imgOpen Access
More Energy-Efficient Turning
Author(s) -
Вячеслав Викторович Фролов,
Ольга Юрьевна Приходько,
Сергей Евгеньевич Слипченко
Publication year - 2021
Publication title -
bulletin of the south ural state university series power engineering
Language(s) - English
Resource type - Journals
eISSN - 2409-1057
pISSN - 1990-8512
DOI - 10.14529/power210106
Subject(s) - machining , process (computing) , power (physics) , grid , energy (signal processing) , machine tool , software , computer science , electricity , engineering , efficient energy use , automotive engineering , mechanical engineering , electrical engineering , operating system , statistics , physics , geometry , mathematics , quantum mechanics
This study is a search for more energy-efficient turning enabled by optimal utilization of the machining facility’s power grid. To that end, the authors have (i) modeled the machining facility on the principles of object-oriented design; (ii) tested the model for adequacy in real-world applications; (iii) devised an approach to implementing specific processes and related design solutions for more energy-efficient machining. Cluster analysis shows that a typical turning line used in small-batch manufacturing may contain up to 20 machines with the following power distribution: 7 0.75-kW machines, 4 1.5-kW machines, 3 2.2-kW machines, 1 3-kW machine, 1 4-kW machine, 1 5.5-kW machine, 1 7.5-kW machine, and 2 11-kW machines. This configuration enables the most power-efficient turning process. The batch for turning should be distributed by the maximum power factor using the object model for the application as well as the author-developed NET-based software. In this approach, all the machines serve as cluster centers that the machined parts are grouped around to make the tur­ning process more energy-efficient by reducing the in-grid electricity loss.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom