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Manufacturing Stacks: From Reference Models to Technology Stacks for Digital Manufacturing

: Lüdtke, Mathias; Delval, Ludovic; Hechtbauer, Jonathan; Bordignon, Mirko

Postprint urn:nbn:de:0011-n-5856158 (258 KByte PDF)
MD5 Fingerprint: 53934a54d3a724928454afbffb98469d
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Erstellt am: 4.12.2020

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019. Proceedings : Zaragoza, Spain, 10 - 13 September 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0303-7
ISBN: 978-1-7281-0302-0
ISBN: 978-1-7281-0304-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <24, 2019, Zaragoza>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
Automatisierung; digitale Produktion; Fehlerbeseitigung; Fertigungssystem; Software-Architektur

A number of initiatives promote the topic of digital manufacturing, i.e., the adoption of pervasive, fine-grained cyber-physical systems (CPS) and of data-driven optimization techniques within the manufacturing domain, both in order to gain efficiency while running conventional operations and to allow for new production and business models such as mass customization or networked value chains. In this context, we contribute the findings from our experience in pursuing this vision through two projects with different goals (gradual improvement over current operations vs. more ambitious rethink), target maturity of their results (production-grade vs. proof-of-concept pilots), and therefore approaches. Concretely, we first elaborate on the often neglected line-level automation and the importance of making it a first-class citizen in digital manufacturing, especially for transitioning current manufacturing facilities into future scenarios. We describe the approach and reference the specific technologies used for its production deployment on the factory floor at a customer organization. We then introduce the positioning, within a second stack jointly prototyped with partner organizations, of reasoning algorithms. As entry points for added autonomy into manufacturing systems, they pave the way for automatic (re)configuration from high level goals and for error recovery. This contribution provides insights on how to design the next generation of software architectures for manufacturing, and aims to contribute to the transition from early reference architectural models into design blueprints for actual technology stacks.