Die Zusammenarbeit von Menschen und künstlicher Intelligenz (KI) hat große Potenziale für die Flexibilisierung und Produktivität von Produktionsanlagen. Um diese Potenziale ausschöpfen zu können, bedarf es eines Paradigmenwechsels bei der Entwicklung KI-gestützter Produktionsprozesse. Hier stoßen klassische Methoden der Prozessentwicklung häufig an ihre Grenzen. Dieser Beitrag zeigt die relevanten Handlungsebenen für einen menschzentrierten KI-Ansatz sowie mögliche Anknüpfungspunkte für die weitere Forschung auf.
The cooperation of humans and artificial intelligence (AI) has great potential to increase flexibility and productivity of production facilities. To exploit this potential, a paradigm shift is required in the development of AI-supported production processes. Here, classical methods of process development often reach their limits. This article shows the relevant levels of action for a human-centred AI approach as well as possible starting points for further research.
Current trends in the manufacturing industry lead to high competitive pressure and requirements regarding process autonomy and flexibility in the production environment. Especially in assembly, automation systems are confronted with a high number of variants. Robot-based processes are a powerful tool for addressing these challenges. For this purpose, robots must be made capable of grasping a variety of diverse components, which are often provided in unknown poses. In addition to existing analytical algorithms, empirical ML-based approaches have been developed, which offer great potentials in increasing flexibility. In this paper, the functionalities and potentials of these approaches will be presented and then compared to the requirements from production processes in order to analyze the status quo of ML-based grasping. Functional gaps are identified that still need to be overcome in order to enable the technology for the use in industrial assembly.
Die Nutzung von Daten zur Optimierung der Fertigung und zur Erhöhung der Maschinenverfügbarkeit verspricht großes Potenzial. Demgegenüber stellt der Zugriff auf die Daten älterer Maschinen eine nicht zu vernachlässigende Herausforderung dar. Vor allem die hohe Heterogenität der Schnittstellen von Brownfieldanlagen in kleinen und mittleren Unternehmen erschweren das Erfassen der Daten zusätzlich. Durch ein konfigurierbares, kostengünstiges Gateway wird das Anbinden verschiedenster Anlagen erleichtert.
The use of data to optimize manufacturing and increase machine availability promises great potential. In contrast, access to the data of older machines is a challenge that should not be neglected. In particular, the high heterogeneity of interfaces of brownfield machines in small and medium-sized enterprises makes data acquisition even more difficult. A configurable, cost-effective gateway facilitates the connection of a wide variety of plants.
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