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Recapitulation

  • Thorsten WuestEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

Concluding, the presented product state concept allows to identify relevant state drivers of complex manufacturing systems. The concept is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications. This fits nicely with current initiatives like ‘Industrie 4.0’, ‘Cyber Physical Systems’ in Europe and the ‘Industrial Internet’ and ‘Advanced Manufacturing Partnership’ in the US as well as the growing area of Big Data research. It can be safely said that in the near future, the amount of data derived from manufacturing operations will increase due to these developments. This offers both opportunities and challenges for manufacturing companies and manufacturing research. With the developed concept, the increasing data streams can be analyzed efficiently and applicable results can be derived. The analysis results present a direct benefit in form of the most important process parameters and state characteristics, the state drivers, of the manufacturing system. These can be directly utilized in, e.g., quality monitoring and advanced process control.

Keywords

Feature Selection Manufacturing System State Driver Important Process Parameter Develop Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). A gene selection method for cancer classification using support vector machines. Machine Learning, 46, 389–422. doi: 10.1155/2012/586246.CrossRefzbMATHGoogle Scholar
  2. Wuest, T., Hribernik, K., & Thoben, K. (2012). Can a product have a facebook? A new perspective on product avatars in product lifecycle management. In L. Rivest, A. Bouraz, & B. Louhichi (Eds.), Product Lifecycle Management: Towards Knowledge-Rich Enterprises. Proceedings of the 9th International Conference on Product Lifecycle Management. Montréal, Canada.Google Scholar
  3. Wuest, T., Hribernik, K., & Thoben, K. (2013). Digital representations of intelligent products: Product avatar 2.0. In M. Abramovici & R. Stark, M. Abramovici & R. Stark (Eds.), Smart Product Engineering, LNPE (pp. 675–684). Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-30817-8.
  4. Wuest, T., Hribernik, K. & Thoben, K.-D. (2014). Accessing servitization potential of PLM data by applying the product avatar concept. Production Planning and Control (accepted).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ICT Applications for ProductionBIBA—Bremer Institut für Produktion und Logistik GmbHBremenGermany
  2. 2.Department of Production EngineeringUniversity of BremenBremenGermany

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