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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	80-86
Número de páginas	7
ISBN (versión digital)	9781538648292
DOI	https://doi.org/10.1109/INDIN.2018.8472073
Estado	Publicada - 24 sept 2018
Publicado de forma externa	Sí
Evento	16th IEEE International Conference on Industrial Informatics, INDIN 2018 - Porto, Portugal Duración: 18 jul 2018 → 20 jul 2018

Serie de la publicación

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

Conferencia

Conferencia	16th IEEE International Conference on Industrial Informatics, INDIN 2018
País/Territorio	Portugal
Ciudad	Porto
Período	18/07/18 → 20/07/18

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 9: Industria, innovación e infraestructura

Acceder al documento

10.1109/INDIN.2018.8472073

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Escartin, J., Argandona, J., & Gerrikagoitia, J. K. (2018). Data-driven Exploration and Process Optimization for a Milling-boring Machine. En Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (pp. 80-86). Artículo 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).

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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\}",

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year = "2018",

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Escartin, J, Argandona, J & Gerrikagoitia, JK 2018, Data-driven Exploration and Process Optimization for a Milling-boring Machine. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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PY - 2018/9/24

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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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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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Escartin J, Argandona J, Gerrikagoitia JK. Data-driven Exploration and Process Optimization for a Milling-boring Machine. En 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

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