A new model for predicting power consumption of machining processes: A turning case

Leilei Meng, Mengchu Zhou, Chaoyong Zhang, Guangdong Tian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Nowadays, discrete manufacturing enterprises are forced to reduce energy consumption owing to high energy cost, growing production demands, and environmental problems. An accurate energy consumption model is needed urgently to achieve this goal. In this paper, a new model based on empirical modeling and experiments is proposed to estimate energy consumption for machining processes. Experimental results show that the new model can make more accurate prediction of energy consumption in comparison with existing models. The new model is of great value to enterprises to develop energy-efficient optimization strategies.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PublisherIEEE Computer Society
Pages1289-1294
Number of pages6
ISBN (Electronic)9781509024094
DOIs
StatePublished - Nov 14 2016
Event2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States
Duration: Aug 21 2016Aug 24 2016

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2016-November
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
Country/TerritoryUnited States
CityFort Worth
Period8/21/168/24/16

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Energy consumption model
  • green production
  • machining process
  • manufacturing process

Fingerprint

Dive into the research topics of 'A new model for predicting power consumption of machining processes: A turning case'. Together they form a unique fingerprint.

Cite this