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Self-optimizing production technologies

: Klocke, Fritz; Abel, Dirk; Gries, Thomas; Hopmann, Christian; Loosen, Peter; Poprawe, Reinhart; Reisgen, Uwe; Schmitt, Robert; Schulz, Wolfgang; Abels, Peter; Adams, Oliver; Auerbach, Thomas; Buchholz, Guido; Döbbeler, Benjamin; Frank, Daniel; Heinisch, Julian; Hermanns, Torsten; Gloy, Yves-Simon; Keitzel, Gunnar; Kemper, Maximilian; Martel, Diana Suarez; Reimer, Viktor; Reiter, Matthias; Saggiomo, Marco; Schwenzer, Max; Stemmler, Sebastian; Stoyanov, Stoyan; Thombansen, Ulrich; Veselovac, Drazen; Willms, Konrad


Brecher, Christian; Özdemir, Denis:
Integrative Production Technology. Theory and Applications
Cham: Springer International Publishing, 2017
ISBN: 978-3-319-47451-9 (Print)
ISBN: 978-3-319-47452-6 (Online)
ISBN: 3-319-47451-0
Book Article
Fraunhofer IPT ()
Fraunhofer ILT ()
automation; Fertigungstechnik; FEM; flexibles Fertigungssystem; Fräsen; machining; Messtechnik; Modellierung; Optimierung; Produktion; Produktionstechnik; Prozessoptimierung; Prozessüberwachung; Roboter; Simulation

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.