FG Automatisierungstechnik
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Institute
This minitrack will address scientific and technical research on how traditional production systems are transformed into next generation smart production systems. It comprises a mix of presentations from leading industries (automotive and aircraft) as well as from well-known research institutions (Fraunhofer society, Germany) and universities (Brandenburg University of Technology Cottbus-Senftenberg, University of Bergamo, Aalborg University).
The minitrack will foster discussions concerning the strategies companies are adopting for managing such transformation, the need for new competences for sustaining it, the implementation of new digital technologies and their impact on the existing production systems.
Digital Business Model Innovation in SMEs - Case Studies with DIH Support from Brandenburg (Germany)
(2020)
The Digital Twin is seen as the step towards digitalization in many areas, including the area of manufacturing which is the focus of this paper. However, the frequently application-oriented development of concepts and initial implementations make it difficult to adapt them to other applications. Especially when production deviates from the usual highly automated processes and requires high flexibility, e.g. to allow batch size one. In such a case, the Digital Twin must support modular shopfloors with flexible areas in which different production times and various tools and machines are used. It should also be possible to react promptly to problems in production. Design errors that only become apparent during production must be reported directly. In order to deal with this problem, the paper presents a concept for an abstract representation of a Digital Shadow (DS). The DS combines the disciplines of production planning and control, product lifecycle and layout planning which creates new links and enables further application scenarios. Based on this Digital Shadow, different applications can be realized that provide user-related solutions. For this purpose, a tree structure is used that links information about the product and the production environment which in this case is variable.
The fast and safe motion of goods and people is one of the foundations of the modern world. Jet aircraft is the fastest transport at the moment along with high-speed trains. Accordingly, both production and maintenance of aircrafts are an important task of our modern industrial environment as well. Modern aircraft engines require appropriate care and understanding of design and manufacturing. This is even more important, as the production of aerospace engines remains a manual process in many cases with limited data sources. Thus, quality control will need to take into account verification of manufacturing and assembly steps through specific checks and controls whilst implementing additional data sources. Automation of tasks still is at a low level. In this article a review of the challenges with regard to controls, automation and process and technical understanding for aerospace engine production and repair is provided. As this requires the collaboration of many teams and partners, an improvement and step change towards deeper understanding and process efficiency is required. As many operations remain manual, innovations for how humans interact with the technology and collaborate with an industrial environment are needed. The project in this article demonstrates the creation and usage of the proposed solutions for collaboration, troubleshooting and error correction.
Even though digitization is a topic of high interest and promises remarkable benefits for the German manufacturing industry, it is still in its development phase. Thus, necessities arising from it are not predictable, yet. A great obstacle that will occur is the appropriate qualification of employees at all levels, sectors and firm sizes. The integration of new technologies will have radical impact on the role and job profile of all staff members as they alter the requirements across the whole value chain and lead to new processes. Latest studies showed that an insufficient qualification of employees is one of the greatest challenges for a successful digitization. Thus, to regain a globally leading position in the manufacturing industry German companies, especially small and medium sized enterprises (SME), need to qualify their employees appropriately. The LTA-FIT model is an approach that aims to face these challenges.
The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best.