Consideration of manufacturing data to apply machine learning methods for predictive manufacturing

Ji Hyeong Han; Su Young Chi

doi:10.1109/ICUFN.2016.7536995

Consideration of manufacturing data to apply machine learning methods for predictive manufacturing

Ji Hyeong Han, Su Young Chi

Dept. of Computer Science and Engineering

Electronics and Telecommunications Research Institute

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

34 Scopus citations

Abstract

According to the recent development of internet of things and big data, the serious tries of implementing smart factory have been increased. To realize the smart factory, firstly predictive manufacturing system should be implemented. As a first step of predictive manufacturing, this paper focuses on solving the simple but time consuming and high cost task in the predictive manner. The target problem of this paper is predicting CNC tool wear compensation offset using machine learning methods based on the data. To apply machine learning methods, we should understand the characteristics of the data and find the most suitable method according to the data characteristics. Thus, this paper discusses the characteristics of manufacturing data and compares various cases of applying machine learning methods.

Original language	English
Title of host publication	ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	109-113
Number of pages	5
ISBN (Electronic)	9781467399913
DOIs	https://doi.org/10.1109/ICUFN.2016.7536995
State	Published - 9 Aug 2016
Event	8th International Conference on Ubiquitous and Future Networks, ICUFN 2016 - Vienna, Austria Duration: 5 Jul 2016 → 8 Jul 2016

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2016-August
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	8th International Conference on Ubiquitous and Future Networks, ICUFN 2016
Country/Territory	Austria
City	Vienna
Period	5/07/16 → 8/07/16

Keywords

CNC tool wear compensation offset
machine learning
Manufacturing data
predictive manufacturing
smart factory

UN SDGs

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

Access to Document

10.1109/ICUFN.2016.7536995

Cite this

Han, J. H., & Chi, S. Y. (2016). Consideration of manufacturing data to apply machine learning methods for predictive manufacturing. In ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks (pp. 109-113). Article 7536995 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2016-August). IEEE Computer Society. https://doi.org/10.1109/ICUFN.2016.7536995

@inproceedings{b412347893ac4e49bb1f6e963ea12ccd,

title = "Consideration of manufacturing data to apply machine learning methods for predictive manufacturing",

abstract = "According to the recent development of internet of things and big data, the serious tries of implementing smart factory have been increased. To realize the smart factory, firstly predictive manufacturing system should be implemented. As a first step of predictive manufacturing, this paper focuses on solving the simple but time consuming and high cost task in the predictive manner. The target problem of this paper is predicting CNC tool wear compensation offset using machine learning methods based on the data. To apply machine learning methods, we should understand the characteristics of the data and find the most suitable method according to the data characteristics. Thus, this paper discusses the characteristics of manufacturing data and compares various cases of applying machine learning methods.",

keywords = "CNC tool wear compensation offset, machine learning, Manufacturing data, predictive manufacturing, smart factory",

author = "Han, \{Ji Hyeong\} and Chi, \{Su Young\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Ubiquitous and Future Networks, ICUFN 2016 ; Conference date: 05-07-2016 Through 08-07-2016",

year = "2016",

month = aug,

day = "9",

doi = "10.1109/ICUFN.2016.7536995",

language = "English",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

pages = "109--113",

booktitle = "ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks",

}

Han, JH & Chi, SY 2016, Consideration of manufacturing data to apply machine learning methods for predictive manufacturing. in ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks., 7536995, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2016-August, IEEE Computer Society, pp. 109-113, 8th International Conference on Ubiquitous and Future Networks, ICUFN 2016, Vienna, Austria, 5/07/16. https://doi.org/10.1109/ICUFN.2016.7536995

Consideration of manufacturing data to apply machine learning methods for predictive manufacturing. / Han, Ji Hyeong; Chi, Su Young.
ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2016. p. 109-113 7536995 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2016-August).

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

TY - GEN

T1 - Consideration of manufacturing data to apply machine learning methods for predictive manufacturing

AU - Han, Ji Hyeong

AU - Chi, Su Young

PY - 2016/8/9

Y1 - 2016/8/9

N2 - According to the recent development of internet of things and big data, the serious tries of implementing smart factory have been increased. To realize the smart factory, firstly predictive manufacturing system should be implemented. As a first step of predictive manufacturing, this paper focuses on solving the simple but time consuming and high cost task in the predictive manner. The target problem of this paper is predicting CNC tool wear compensation offset using machine learning methods based on the data. To apply machine learning methods, we should understand the characteristics of the data and find the most suitable method according to the data characteristics. Thus, this paper discusses the characteristics of manufacturing data and compares various cases of applying machine learning methods.

AB - According to the recent development of internet of things and big data, the serious tries of implementing smart factory have been increased. To realize the smart factory, firstly predictive manufacturing system should be implemented. As a first step of predictive manufacturing, this paper focuses on solving the simple but time consuming and high cost task in the predictive manner. The target problem of this paper is predicting CNC tool wear compensation offset using machine learning methods based on the data. To apply machine learning methods, we should understand the characteristics of the data and find the most suitable method according to the data characteristics. Thus, this paper discusses the characteristics of manufacturing data and compares various cases of applying machine learning methods.

KW - CNC tool wear compensation offset

KW - machine learning

KW - Manufacturing data

KW - predictive manufacturing

KW - smart factory

UR - https://www.scopus.com/pages/publications/84983347901

U2 - 10.1109/ICUFN.2016.7536995

DO - 10.1109/ICUFN.2016.7536995

M3 - Conference contribution

AN - SCOPUS:84983347901

T3 - International Conference on Ubiquitous and Future Networks, ICUFN

SP - 109

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BT - ICUFN 2016 - 8th International Conference on Ubiquitous and Future Networks

PB - IEEE Computer Society

T2 - 8th International Conference on Ubiquitous and Future Networks, ICUFN 2016

Y2 - 5 July 2016 through 8 July 2016

ER -

Consideration of manufacturing data to apply machine learning methods for predictive manufacturing

Abstract

Publication series

Conference

Keywords

UN SDGs

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