Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry

Goncalo Candido, Jose Barata, Sebastian Scholze, Oliver Kotte, Dragan Stokic

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

2 Citations (Scopus)

Abstract

The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.

Original languageEnglish
Title of host publication2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN)
PublisherIEEE
Pages386-391
Number of pages6
Publication statusPublished - 2013
Event11th IEEE International Conference on Industrial Informatics (INDIN) - Bochum, Germany
Duration: 29 Jul 201331 Jul 2013

Publication series

NameIEEE International Conference on Industrial Informatics INDIN
PublisherIEEE
ISSN (Print)1935-4576

Conference

Conference11th IEEE International Conference on Industrial Informatics (INDIN)
CountryGermany
CityBochum
Period29/07/1331/07/13

Keywords

  • NETWORK

Cite this

Candido, G., Barata, J., Scholze, S., Kotte, O., & Stokic, D. (2013). Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry. In 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) (pp. 386-391). (IEEE International Conference on Industrial Informatics INDIN). IEEE.
Candido, Goncalo ; Barata, Jose ; Scholze, Sebastian ; Kotte, Oliver ; Stokic, Dragan. / Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry. 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN). IEEE, 2013. pp. 386-391 (IEEE International Conference on Industrial Informatics INDIN).
@inproceedings{36620b815e284754af8ca83aa108605f,
title = "Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry",
abstract = "The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.",
keywords = "NETWORK",
author = "Goncalo Candido and Jose Barata and Sebastian Scholze and Oliver Kotte and Dragan Stokic",
note = "Sem PDF. European Union (NMP-2008-228857) FCT Fundacao para a Ciencia e Tecnologia (OE/EEI/UI0066/2011)",
year = "2013",
language = "English",
series = "IEEE International Conference on Industrial Informatics INDIN",
publisher = "IEEE",
pages = "386--391",
booktitle = "2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN)",

}

Candido, G, Barata, J, Scholze, S, Kotte, O & Stokic, D 2013, Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry. in 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN). IEEE International Conference on Industrial Informatics INDIN, IEEE, pp. 386-391, 11th IEEE International Conference on Industrial Informatics (INDIN), Bochum, Germany, 29/07/13.

Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry. / Candido, Goncalo; Barata, Jose; Scholze, Sebastian; Kotte, Oliver; Stokic, Dragan.

2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN). IEEE, 2013. p. 386-391 (IEEE International Conference on Industrial Informatics INDIN).

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

TY - GEN

T1 - Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry

AU - Candido, Goncalo

AU - Barata, Jose

AU - Scholze, Sebastian

AU - Kotte, Oliver

AU - Stokic, Dragan

N1 - Sem PDF. European Union (NMP-2008-228857) FCT Fundacao para a Ciencia e Tecnologia (OE/EEI/UI0066/2011)

PY - 2013

Y1 - 2013

N2 - The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.

AB - The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.

KW - NETWORK

M3 - Conference contribution

T3 - IEEE International Conference on Industrial Informatics INDIN

SP - 386

EP - 391

BT - 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN)

PB - IEEE

ER -

Candido G, Barata J, Scholze S, Kotte O, Stokic D. Self-Learning Production Systems (SLPS) - Optimization of Manufacturing process parameters for the Shoe Industry. In 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN). IEEE. 2013. p. 386-391. (IEEE International Conference on Industrial Informatics INDIN).