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Manufacturing systems of the future: a multi-disciplinary approach

Manufacturing systems of the future: a multi-disciplinary approach
Manufacturing systems of the future: a multi-disciplinary approach
Successful manufacturing systems for the future have to be based on know-how originating from more than the traditional manufacturing domains. Approaches such as Reconfigurable Manufacturing Systems, the Agile Assembly Architecture and Holonic Manufacturing Systems go into the right direction; combined with approaches known from Mobile Robotics, Collective Artificial Intelligence and Complexity Science, there is considerable potential for creative solutions to the problems of low volume – high change productions. Systems must be enabled to selforganize, take profit of emergence and become more autonomous.More due to human factors than due to technical reasons, system autonomy and emergence belong to industry's worst nightmares. It is therefore crucial to address this fear while at the same time working on reliable methods and tools.
239-244
Springer
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Barata, Jose
f529ed38-f9be-466e-bd68-dec2cf876f58
Azevedo, Americo
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Barata, Jose
f529ed38-f9be-466e-bd68-dec2cf876f58
Azevedo, Americo

Frei, Regina and Barata, Jose (2008) Manufacturing systems of the future: a multi-disciplinary approach. In, Azevedo, Americo (ed.) Innovation in Manufacturing Networks. (IFIP – The International Federation for Information Processing, , (doi:10.1007/978-0-387-09492-2_26), 266) Boston, MA. Springer, pp. 239-244. (doi:10.1007/978-0-387-09492-2_26).

Record type: Book Section

Abstract

Successful manufacturing systems for the future have to be based on know-how originating from more than the traditional manufacturing domains. Approaches such as Reconfigurable Manufacturing Systems, the Agile Assembly Architecture and Holonic Manufacturing Systems go into the right direction; combined with approaches known from Mobile Robotics, Collective Artificial Intelligence and Complexity Science, there is considerable potential for creative solutions to the problems of low volume – high change productions. Systems must be enabled to selforganize, take profit of emergence and become more autonomous.More due to human factors than due to technical reasons, system autonomy and emergence belong to industry's worst nightmares. It is therefore crucial to address this fear while at the same time working on reliable methods and tools.

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Published date: 2008

Identifiers

Local EPrints ID: 433315
URI: http://eprints.soton.ac.uk/id/eprint/433315
PURE UUID: cd2ac072-c3fd-44b7-bfd4-6c8fe1c42ca4
ORCID for Regina Frei: ORCID iD orcid.org/0000-0002-0953-6413

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Date deposited: 14 Aug 2019 16:30
Last modified: 25 Oct 2019 00:21

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