Data-driven Exploration and Process Optimization for a Milling-boring Machine

Javier Escartin; Jon Argandona; Jon Kepa Gerrikagoitia

doi:10.1109/INDIN.2018.8472073

Data-driven Exploration and Process Optimization for a Milling-boring Machine

Javier Escartin
, Jon Argandona
, Jon Kepa Gerrikagoitia

IDEKO

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

Abstract

The incorporation of cyber-physical systems to manufacturing enables the collection and storage of big amounts of machine data. These data, properly studied, provide useful information about the machine, its use and its state. In this work a use case about the utilization of machine data for maintenance and process optimization for a milling-boring machine is presented. First, some guidelines on data exploration and machine operation identification from raw machine data are introduced. Then, the results of this exploration are used to compute some descriptive statistics and to train a machine learning model. From this data analysis some conclusions about the relation of the spindle vibration with other machine variables are drawn.

Original language	English
Title of host publication	Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	80-86
Number of pages	7
ISBN (Electronic)	9781538648292
DOIs	https://doi.org/10.1109/INDIN.2018.8472073
Publication status	Published - 24 Sept 2018
Externally published	Yes
Event	16th IEEE International Conference on Industrial Informatics, INDIN 2018 - Porto, Portugal Duration: 18 Jul 2018 → 20 Jul 2018

Publication series

Name	Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018

Conference

Conference	16th IEEE International Conference on Industrial Informatics, INDIN 2018
Country/Territory	Portugal
City	Porto
Period	18/07/18 → 20/07/18

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/INDIN.2018.8472073

Cite this

Escartin, J., Argandona, J., & Gerrikagoitia, J. K. (2018). Data-driven Exploration and Process Optimization for a Milling-boring Machine. In Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (pp. 80-86). Article 8472073 (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDIN.2018.8472073

Escartin, Javier ; Argandona, Jon ; Gerrikagoitia, Jon Kepa. / Data-driven Exploration and Process Optimization for a Milling-boring Machine. Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 80-86 (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018).

@inproceedings{c8718374ae3e4f119d86e1ce5dec9785,

title = "Data-driven Exploration and Process Optimization for a Milling-boring Machine",

abstract = "The incorporation of cyber-physical systems to manufacturing enables the collection and storage of big amounts of machine data. These data, properly studied, provide useful information about the machine, its use and its state. In this work a use case about the utilization of machine data for maintenance and process optimization for a milling-boring machine is presented. First, some guidelines on data exploration and machine operation identification from raw machine data are introduced. Then, the results of this exploration are used to compute some descriptive statistics and to train a machine learning model. From this data analysis some conclusions about the relation of the spindle vibration with other machine variables are drawn.",

author = "Javier Escartin and Jon Argandona and Gerrikagoitia, \{Jon Kepa\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 16th IEEE International Conference on Industrial Informatics, INDIN 2018 ; Conference date: 18-07-2018 Through 20-07-2018",

year = "2018",

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doi = "10.1109/INDIN.2018.8472073",

language = "English",

series = "Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018",

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Escartin, J, Argandona, J & Gerrikagoitia, JK 2018, Data-driven Exploration and Process Optimization for a Milling-boring Machine. in Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018., 8472073, Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018, Institute of Electrical and Electronics Engineers Inc., pp. 80-86, 16th IEEE International Conference on Industrial Informatics, INDIN 2018, Porto, Portugal, 18/07/18. https://doi.org/10.1109/INDIN.2018.8472073

Data-driven Exploration and Process Optimization for a Milling-boring Machine. / Escartin, Javier; Argandona, Jon; Gerrikagoitia, Jon Kepa.
Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 80-86 8472073 (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018).

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

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Y1 - 2018/9/24

N2 - The incorporation of cyber-physical systems to manufacturing enables the collection and storage of big amounts of machine data. These data, properly studied, provide useful information about the machine, its use and its state. In this work a use case about the utilization of machine data for maintenance and process optimization for a milling-boring machine is presented. First, some guidelines on data exploration and machine operation identification from raw machine data are introduced. Then, the results of this exploration are used to compute some descriptive statistics and to train a machine learning model. From this data analysis some conclusions about the relation of the spindle vibration with other machine variables are drawn.

AB - The incorporation of cyber-physical systems to manufacturing enables the collection and storage of big amounts of machine data. These data, properly studied, provide useful information about the machine, its use and its state. In this work a use case about the utilization of machine data for maintenance and process optimization for a milling-boring machine is presented. First, some guidelines on data exploration and machine operation identification from raw machine data are introduced. Then, the results of this exploration are used to compute some descriptive statistics and to train a machine learning model. From this data analysis some conclusions about the relation of the spindle vibration with other machine variables are drawn.

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U2 - 10.1109/INDIN.2018.8472073

DO - 10.1109/INDIN.2018.8472073

M3 - Conference contribution

AN - SCOPUS:85055538961

T3 - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018

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BT - Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Industrial Informatics, INDIN 2018

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ER -

Escartin J, Argandona J, Gerrikagoitia JK. Data-driven Exploration and Process Optimization for a Milling-boring Machine. In Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 80-86. 8472073. (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018). doi: 10.1109/INDIN.2018.8472073

Data-driven Exploration and Process Optimization for a Milling-boring Machine

Abstract

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