How Virtual Production Intelligence Can Improve Laser-Cutting Planning Processes

Rudolf Reinhard (), Urs Eppelt, Toufik Al-Khawly, Tobias Meisen, Daniel Schilberg, Wolfgang Schulz and Sabina Jeschke
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Rudolf Reinhard: IMA/ZLW & IfU, RWTH Aachen University
Urs Eppelt: IMA/ZLW & IfU, RWTH Aachen University
Toufik Al-Khawly: IMA/ZLW & IfU, RWTH Aachen University
Tobias Meisen: IMA/ZLW & IfU, RWTH Aachen University
Daniel Schilberg: IMA/ZLW & IfU, RWTH Aachen University
Wolfgang Schulz: IMA/ZLW & IfU, RWTH Aachen University
Sabina Jeschke: IMA/ZLW & IfU, RWTH Aachen University

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2015/2016, 2016, pp 941-955 from Springer

Abstract: Abstract The complexity of modern production conditions demands integrative approaches in the fields of simulation and analysis to improve product quality and production efficiency. Existing concepts of virtual production meet this need to some extent. However, problems of application interoperability and data compatibility remain. One approach is the definition of a standardized file format, which is costly to create and to maintain. Other approaches avoid the need for a uniform standard by mapping data structures onto a canonical data model. Although these methods allow for simulation and examination of individual elements, the analysis of the integrated process remains a challenge. Here, the data analysis solutions from the field of the so-called intelligence-solutions can prove useful. Within this paper, a use case scenario taken from the field of laser cutting is presented. Herein, the planning for laser cutting is conducted in a modular format. A new concept is presented that addresses the requirements aforementioned and that conforms to the principles of the integration and examination of data. The new concept, called Virtual Production Intelligence, is formed by combining the concept of virtual production with “intelligence solutions” or the goal of gaining knowledge through the analysis of already completed processes.

Keywords: Application Integration; Data Analysis; Decision Support; Digital Factory (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-42620-4_70

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DOI: 10.1007/978-3-319-42620-4_70

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