Energy Efficiency in Machine Tools - A Self-Learning Approach

Giovanni Di Orio, Goncalo Candido, Jose Barata, Jose Luiz Bittencourt, Ralf Bonefeld

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

9 Citations (Scopus)

Abstract

Due to the growing demand to reduce the environmental impact, the manufacturing companies of today are encouraged to adopt new green methodologies, strategies and technologies for increasing the energy efficiency of their manufacturing production lines. These solutions have a great impact on several productivity metrics including availability and costs. The continuous pursuit of productivity and particularly of machine availability has led to an increase of the total energy consumption in production plants. However, productivity gains can also be achieved by reducing the life- cycle costs of the manufacturing production systems. The research currently done under the scope of Self- Learning Production Systems (SLPS) tries to fill the gap between availability and efficiency by providing an innovative and integrated approach for ensuring the efficient utilization of the resources in machine tools.

Original languageEnglish
Title of host publication2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013)
PublisherIEEE
Pages4878-4883
Number of pages6
ISBN (Electronic)978-1-4799-0652-9
DOIs
Publication statusPublished - 2013
EventIEEE International Conference on Systems, Man, and Cybernetics (SMC) - Manchester
Duration: 13 Oct 201316 Oct 2013

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference Proceedings
PublisherIEEE
ISSN (Print)1062-922X

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics (SMC)
CityManchester
Period13/10/1316/10/13

Keywords

  • Machine Tool
  • Energy Efficiency
  • Data Mining
  • Context Awareness
  • Service Oriented Architecture

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