Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation

Ji Hyeong Han; Rockwon Kim; Su Young Chi

doi:10.1145/2837060.2837066

Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation

Ji Hyeong Han
, Rockwon Kim
, 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

2 Scopus citations

Abstract

The manufacturing industry has become more competitive because of globalization and fast change in the industry. To survive from the global market, manufacturing enterprises should reduce the product cost and increase the productivity. The most promising way is applying the information communication technology especially machine learning algorithms to the traditional manufacturing system. This paper presents recent trends of applying machine learning techniques to manufacturing system and briey explains each kind of applications. As a representative application of machine learning algorithms to manufacturing system, a generalized tool wear compensation parameter recommendation framework using regression algorithms and preliminary results using real data gathered from local and small manufacturing are also presented.

Original language	English
Title of host publication	Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015
Editors	Aziz Nasridinov, Carson K. Leung
Publisher	Association for Computing Machinery
Pages	51-57
Number of pages	7
ISBN (Electronic)	9781450338462
DOIs	https://doi.org/10.1145/2837060.2837066
State	Published - 20 Oct 2015
Event	2015 International Conference on Big Data Applications and Services, BigDAS 2015 - Jeju Island, Korea, Republic of Duration: 20 Oct 2015 → 23 Oct 2015

Publication series

Name	ACM International Conference Proceeding Series
Volume	20-23-October-2015

Conference

Conference	2015 International Conference on Big Data Applications and Services, BigDAS 2015
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/10/15 → 23/10/15

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Machine learning
Predictive manufacturing
Tool wear compensation parameter recommendation

Access to Document

10.1145/2837060.2837066

Cite this

Han, J. H., Kim, R., & Chi, S. Y. (2015). Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation. In A. Nasridinov, & C. K. Leung (Eds.), Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015 (pp. 51-57). (ACM International Conference Proceeding Series; Vol. 20-23-October-2015). Association for Computing Machinery. https://doi.org/10.1145/2837060.2837066

Han, Ji Hyeong ; Kim, Rockwon ; Chi, Su Young. / Applications of machine learning algorithms to predictive manufacturing : Trends and application of tool wear compensation parameter recommendation. Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015. editor / Aziz Nasridinov ; Carson K. Leung. Association for Computing Machinery, 2015. pp. 51-57 (ACM International Conference Proceeding Series).

@inproceedings{395aecbcb1b943a8aabd6e1cbe79045e,

title = "Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation",

abstract = "The manufacturing industry has become more competitive because of globalization and fast change in the industry. To survive from the global market, manufacturing enterprises should reduce the product cost and increase the productivity. The most promising way is applying the information communication technology especially machine learning algorithms to the traditional manufacturing system. This paper presents recent trends of applying machine learning techniques to manufacturing system and briey explains each kind of applications. As a representative application of machine learning algorithms to manufacturing system, a generalized tool wear compensation parameter recommendation framework using regression algorithms and preliminary results using real data gathered from local and small manufacturing are also presented.",

keywords = "Machine learning, Predictive manufacturing, Tool wear compensation parameter recommendation",

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

note = "Publisher Copyright: {\textcopyright}2015 ACM.; 2015 International Conference on Big Data Applications and Services, BigDAS 2015 ; Conference date: 20-10-2015 Through 23-10-2015",

year = "2015",

month = oct,

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doi = "10.1145/2837060.2837066",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

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booktitle = "Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015",

}

Han, JH, Kim, R & Chi, SY 2015, Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation. in A Nasridinov & CK Leung (eds), Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015. ACM International Conference Proceeding Series, vol. 20-23-October-2015, Association for Computing Machinery, pp. 51-57, 2015 International Conference on Big Data Applications and Services, BigDAS 2015, Jeju Island, Korea, Republic of, 20/10/15. https://doi.org/10.1145/2837060.2837066

Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation. / Han, Ji Hyeong; Kim, Rockwon; Chi, Su Young.
Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015. ed. / Aziz Nasridinov; Carson K. Leung. Association for Computing Machinery, 2015. p. 51-57 (ACM International Conference Proceeding Series; Vol. 20-23-October-2015).

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

TY - GEN

T1 - Applications of machine learning algorithms to predictive manufacturing

T2 - 2015 International Conference on Big Data Applications and Services, BigDAS 2015

AU - Han, Ji Hyeong

AU - Kim, Rockwon

AU - Chi, Su Young

PY - 2015/10/20

Y1 - 2015/10/20

N2 - The manufacturing industry has become more competitive because of globalization and fast change in the industry. To survive from the global market, manufacturing enterprises should reduce the product cost and increase the productivity. The most promising way is applying the information communication technology especially machine learning algorithms to the traditional manufacturing system. This paper presents recent trends of applying machine learning techniques to manufacturing system and briey explains each kind of applications. As a representative application of machine learning algorithms to manufacturing system, a generalized tool wear compensation parameter recommendation framework using regression algorithms and preliminary results using real data gathered from local and small manufacturing are also presented.

AB - The manufacturing industry has become more competitive because of globalization and fast change in the industry. To survive from the global market, manufacturing enterprises should reduce the product cost and increase the productivity. The most promising way is applying the information communication technology especially machine learning algorithms to the traditional manufacturing system. This paper presents recent trends of applying machine learning techniques to manufacturing system and briey explains each kind of applications. As a representative application of machine learning algorithms to manufacturing system, a generalized tool wear compensation parameter recommendation framework using regression algorithms and preliminary results using real data gathered from local and small manufacturing are also presented.

KW - Machine learning

KW - Predictive manufacturing

KW - Tool wear compensation parameter recommendation

UR - https://www.scopus.com/pages/publications/84996805774

U2 - 10.1145/2837060.2837066

DO - 10.1145/2837060.2837066

M3 - Conference contribution

AN - SCOPUS:84996805774

T3 - ACM International Conference Proceeding Series

SP - 51

EP - 57

BT - Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015

A2 - Nasridinov, Aziz

A2 - Leung, Carson K.

PB - Association for Computing Machinery

Y2 - 20 October 2015 through 23 October 2015

ER -

Han JH, Kim R, Chi SY. Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation. In Nasridinov A, Leung CK, editors, Proceedings of the 2015 International Conference on Big Data Applications and Services, BigDAS 2015. Association for Computing Machinery. 2015. p. 51-57. (ACM International Conference Proceeding Series). doi: 10.1145/2837060.2837066

Applications of machine learning algorithms to predictive manufacturing: Trends and application of tool wear compensation parameter recommendation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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