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Advanced data enrichment and data analysis in manufacturing industry by an example of laser drilling process

 
: Wang, Y.; Tercan, H.; Thiele, T.; Meisen, T.; Jeschke, S.; Schulz, W.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Communications Society; International Telecommunication Union -ITU-:
Challenges for a data-driven society. ITU Kaleidoscope Academic Conference 2017. Proceedings : Nanjing, China, 27-29 November 2017
Piscataway, NJ: IEEE, 2017
ISBN: 978-9-2612-4281-7
ISBN: 978-1-5386-1951-3
ISBN: 978-92-61-24291-6
ISBN: 978-92-61-24301-2
S.61-65
International Telecommunication Union (ITU Kaleidoscope Academic Conference) <9, 2017, Nanjing>
Englisch
Konferenzbeitrag
Fraunhofer ILT ()

Abstract
Nowadays, the internet of things and industry 4.0 from Germany are all focused on the application of data analytics and Artificial Intelligence to build the succeeding generation of manufacturing industry. In manufacturing planning and iterative designing process, the data-driven issues exist in the context of the purpose for approaching the optimal design and generating an explicit knowledge. The multiphysical phenomena, the time consuming comprehensive numerical simulation, and a limited number of experiments lead to the so-called sparse data problems or “curse of dimensionality”. In this work, an advanced technique using reduced models to enrich sparse data is proposed and discussed. The validated reduced models, which are created by several model reduction techniques, are able to generate dense data within an acceptable time. Afterwards, machine learning and data analytics techniques are applied to extract unknown but useful knowledge from the dense data in the Virtual Production Intelligence (VPI) platform. The demonstrated example is a typical case from laser drilling process.

: http://publica.fraunhofer.de/dokumente/N-507340.html