Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process

Qinge Xiao, Congbo Li, Ying Tang, Yanbin Du, Yang Kou

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

6 Scopus citations

Abstract

This paper studies a predictive modeling for cutting energy consumption in CNC turning process by using deep learning methods. An analysis of energy consumption in cutting period is firstly presented, based on which the impact factors of energy are clarified. Then the data collection platform and data pre-processing are introduced, followed by a brief review of Convolutional Neural Network (CNN), Stacked Auto-Encoder (SAE) and Deep Belief Network (DBN). These modeling methods are tested by k-fold cross-validation. The obtained results show that SAE is the most suitable method to model the relationship between process parameters, machining configuration and cutting energy.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1398-1403
Number of pages6
ISBN (Electronic)9781538666500
DOIs
StatePublished - Jan 16 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: Oct 7 2018Oct 10 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/TerritoryJapan
CityMiyazaki
Period10/7/1810/10/18

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

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