3 Citations (Scopus)

Abstract

In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.

Original languageEnglish
Title of host publicationProceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages815-821
Number of pages7
ISBN (Electronic)978-153864829-2
DOIs
Publication statusPublished - 24 Sep 2018
Event16th IEEE International Conference on Industrial Informatics, INDIN 2018 - Porto, Portugal
Duration: 18 Jul 201820 Jul 2018

Conference

Conference16th IEEE International Conference on Industrial Informatics, INDIN 2018
CountryPortugal
CityPorto
Period18/07/1820/07/18

Fingerprint

Interoperability
Data structures
Defects
Manufacturing

Keywords

  • Cyber-physical system
  • Data model
  • Multistage
  • Quality control
  • Zero defect manufacturing

Cite this

Peres, R., Rocha, A. D., Matos, J. P., & Barata, J. (2018). GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. In Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (pp. 815-821). [8472017] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDIN.2018.8472017
Peres, Ricardo ; Rocha, André Dionisio ; Matos, João Pedro ; Barata, José. / GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 815-821
@inproceedings{34ee6669a28842acb866221da779ad8a,
title = "GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing",
abstract = "In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.",
keywords = "Cyber-physical system, Data model, Multistage, Quality control, Zero defect manufacturing",
author = "Ricardo Peres and Rocha, {Andr{\'e} Dionisio} and Matos, {Jo{\~a}o Pedro} and Jos{\'e} Barata",
note = "The GO0DMAN project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 723764.",
year = "2018",
month = "9",
day = "24",
doi = "10.1109/INDIN.2018.8472017",
language = "English",
pages = "815--821",
booktitle = "Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Peres, R, Rocha, AD, Matos, JP & Barata, J 2018, GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. in Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018., 8472017, Institute of Electrical and Electronics Engineers Inc., pp. 815-821, 16th IEEE International Conference on Industrial Informatics, INDIN 2018, Porto, Portugal, 18/07/18. https://doi.org/10.1109/INDIN.2018.8472017

GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. / Peres, Ricardo; Rocha, André Dionisio; Matos, João Pedro; Barata, José.

Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 815-821 8472017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing

AU - Peres, Ricardo

AU - Rocha, André Dionisio

AU - Matos, João Pedro

AU - Barata, José

N1 - The GO0DMAN project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 723764.

PY - 2018/9/24

Y1 - 2018/9/24

N2 - In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.

AB - In the last decades, several research initiatives suggested new solutions regarding the interoperability and interconnectivity among heterogeneous production components and all the actors that somehow interact within the shop-floor. However, most of the proposed data representations are focused on the description of the production capabilities. In this paper, it is proposed a common data model focused not only in the production capabilities of the different components as well as the description of all the events, variables and resources that could indicate quality issues. Hence, the proposed data model describes all the information required by the GO0DMAN solution to reduce as much as possible, the defects, the respective causes and the strategies to avoid the propagation along the line. In order to increase the adoption of the proposed data model, it was developed using AutomationML. The proposed data model was designed and tested within the scope of the Horizon 2020 GO0DMAN project.

KW - Cyber-physical system

KW - Data model

KW - Multistage

KW - Quality control

KW - Zero defect manufacturing

UR - http://www.scopus.com/inward/record.url?scp=85055499910&partnerID=8YFLogxK

U2 - 10.1109/INDIN.2018.8472017

DO - 10.1109/INDIN.2018.8472017

M3 - Conference contribution

SP - 815

EP - 821

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Peres R, Rocha AD, Matos JP, Barata J. GO0DMAN Data Model - Interoperability in Multistage Zero Defect Manufacturing. In Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 815-821. 8472017 https://doi.org/10.1109/INDIN.2018.8472017