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

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

doi:10.1109/SMC.2018.00244

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

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

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1398-1403
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00244
State	Published - Jul 2 2018
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: Oct 7 2018 → Oct 10 2018

Publication series

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

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	10/7/18 → 10/10/18

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/SMC.2018.00244

Cite this

Xiao, Q., Li, C., Tang, Y., Du, Y., & Kou, Y. (2018). Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 1398-1403). Article 8616240 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00244

Xiao, Qinge ; Li, Congbo ; Tang, Ying et al. / Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1398-1403 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

@inproceedings{548a623160fa448e92f04020e4820770,

title = "Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process",

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.",

author = "Qinge Xiao and Congbo Li and Ying Tang and Yanbin Du and Yang Kou",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

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doi = "10.1109/SMC.2018.00244",

language = "English (US)",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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}

Xiao, Q, Li, C, Tang, Y, Du, Y & Kou, Y 2018, Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616240, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1398-1403, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 10/7/18. https://doi.org/10.1109/SMC.2018.00244

Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process. / Xiao, Qinge; Li, Congbo; Tang, Ying et al.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1398-1403 8616240 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process

AU - Xiao, Qinge

AU - Li, Congbo

AU - Tang, Ying

AU - Du, Yanbin

AU - Kou, Yang

PY - 2018/7/2

Y1 - 2018/7/2

N2 - 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.

AB - 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.

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DO - 10.1109/SMC.2018.00244

M3 - Conference contribution

AN - SCOPUS:85062241944

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 1398

EP - 1403

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

PB - Institute of Electrical and Electronics Engineers Inc.

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Xiao Q, Li C, Tang Y, Du Y, Kou Y. Deep Learning Based Modeling for Cutting Energy Consumed in CNC Turning Process. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1398-1403. 8616240. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00244

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

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