Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining

Chen Lun Fan; Jehn Ruey Jiang

doi:10.1109/ECICE47484.2019.8942705

Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining

Chen Lun Fan, Jehn Ruey Jiang

Department of Computer Science and Information Engineering

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

6 Scopus citations

Abstract

This paper proposes methods for predicting the WEDM surface roughness by analyzing manufacturing parameters which are set before production and machining conditions that are gathered during production. The methods are based on the deep neural network (DNN), and the Markov chain integrated with deep neural network (MC-DNN), respectively. The prediction errors of the proposed methods are evaluated and compared with those of related methods in terms of the mean absolute percentage error (MAPE). The comparison results show that the proposed methods have comparatively good performance.

Original language	English
Title of host publication	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Editors	Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	191-194
Number of pages	4
ISBN (Electronic)	9781728125015
DOIs	https://doi.org/10.1109/ECICE47484.2019.8942705
State	Published - Oct 2019
Event	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan Duration: 3 Oct 2019 → 6 Oct 2019

Publication series

Name	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

Conference

Conference	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Country/Territory	Taiwan
City	Yunlin
Period	3/10/19 → 6/10/19

Keywords

Markov chain
deep neural network
surface roughness
wire electrical discharge machining

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ECICE47484.2019.8942705

Cite this

Fan, C. L., & Jiang, J. R. (2019). Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining. In T.-H. Meen (Ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 (pp. 191-194). Article 8942705 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECICE47484.2019.8942705

Fan, Chen Lun ; Jiang, Jehn Ruey. / Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. editor / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 191-194 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

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title = "Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining",

abstract = "This paper proposes methods for predicting the WEDM surface roughness by analyzing manufacturing parameters which are set before production and machining conditions that are gathered during production. The methods are based on the deep neural network (DNN), and the Markov chain integrated with deep neural network (MC-DNN), respectively. The prediction errors of the proposed methods are evaluated and compared with those of related methods in terms of the mean absolute percentage error (MAPE). The comparison results show that the proposed methods have comparatively good performance.",

keywords = "Markov chain, deep neural network, surface roughness, wire electrical discharge machining",

author = "Fan, {Chen Lun} and Jiang, {Jehn Ruey}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 ; Conference date: 03-10-2019 Through 06-10-2019",

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series = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",

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Fan, CL & Jiang, JR 2019, Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining. in T-H Meen (ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019., 8942705, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 191-194, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Yunlin, Taiwan, 3/10/19. https://doi.org/10.1109/ECICE47484.2019.8942705

Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining. / Fan, Chen Lun; Jiang, Jehn Ruey.
2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. p. 191-194 8942705 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

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

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AU - Fan, Chen Lun

AU - Jiang, Jehn Ruey

PY - 2019/10

Y1 - 2019/10

N2 - This paper proposes methods for predicting the WEDM surface roughness by analyzing manufacturing parameters which are set before production and machining conditions that are gathered during production. The methods are based on the deep neural network (DNN), and the Markov chain integrated with deep neural network (MC-DNN), respectively. The prediction errors of the proposed methods are evaluated and compared with those of related methods in terms of the mean absolute percentage error (MAPE). The comparison results show that the proposed methods have comparatively good performance.

AB - This paper proposes methods for predicting the WEDM surface roughness by analyzing manufacturing parameters which are set before production and machining conditions that are gathered during production. The methods are based on the deep neural network (DNN), and the Markov chain integrated with deep neural network (MC-DNN), respectively. The prediction errors of the proposed methods are evaluated and compared with those of related methods in terms of the mean absolute percentage error (MAPE). The comparison results show that the proposed methods have comparatively good performance.

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KW - deep neural network

KW - surface roughness

KW - wire electrical discharge machining

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U2 - 10.1109/ECICE47484.2019.8942705

DO - 10.1109/ECICE47484.2019.8942705

M3 - 會議論文篇章

AN - SCOPUS:85078016475

T3 - 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

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BT - 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 3 October 2019 through 6 October 2019

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Fan CL, Jiang JR. Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining. In Meen TH, editor, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 191-194. 8942705. (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). doi: 10.1109/ECICE47484.2019.8942705

Surface Roughness Prediction Based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining

Abstract

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Conference

Keywords

UN SDGs

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