Projektlogo Unbemanntes Luftfahrtsystem zur Bestandserfassung und Qualitätsprüfung von Paletteninhalten im Blocklager

DroneStock

Unmanned aerial system for inventory recording and quality inspection of pallet contents in indoor block warehouses

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In this project we develop an unmanned aerial system (UAS) for automatic inventory recording and quality inspection of pallet contents in indoor block warehouses. The UAS should be able navigate autonomously through the block warehouse without the need for separation from humans or other autonomous systems. Calculations that require large computational effort, like e.g. image processing, are shifted to a mobile server, which is positioned inside the warehouse and comes with its own Wi-Fi network. This enables the use of more cost-effective drones and improves the scalability of the system.

Duration 01.07.2021 - 30.06.2023, Funded by

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Projektlogo Automatisches Spezifikationstool für die prozessspezifische Auswahl von FTS zur industriellen Einführung in KMU
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Astradis

Automated Specification Tool for AGV Deployment in SMBs

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In this research project, a tool is being developed to support the introduction of automated guided vehicles (AGVs). This includes a guided process and requirements analysis, in which the relevant data is determined and automatically compiled in a formated document for quotation requests. In addition, a manufacturer-independent catalog of AGVs available on the market is created, which can be automatically compared with the determined requirements in order to suggest suitable solutions to the user. Finally, the selection can be validated through the connection to a material flow simulation.

Duration 15.06.2021 - 30.09.2022, Funded by EFRE: Europäischer Fonds für regionale Entwicklung
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Projektlogo Ressourcenbezogene Prozessverwaltung durch flexible Nutzung intelligenter Module in der hybriden Montage

PassForM

Resource-based process management through flexible use of intelligent modules in hybrid assembly

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The PassForM research project creates a modularly reconfigurable assembly station. It allows for a more flexible design of manual and hybrid assembly stations and systematic automation, which improves scalability, re-usability and responsiveness to market developments. Bidirectional information and control instruction exchange enable and ease the integration of the modular assembly stations into existing assembly organizations. For this purpose, a material supply module, a conveyor module and a robot module are implemented. The goal is to unite the opposing requirements of productivity and flexibility in the assembly area of medium quantities. The project will fill the gap between manual and highly automated processes. The performance of the modular, hybrid assembly system will be evaluated and based on application scenarios in variant assembly groups.

Duration 01.06.2021 - 31.05.2023, Funded by AiF

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Baeckerei 4.0

Development of a raw material-specific and cross-process production control in medium-sized bakeries using artificial intelligence

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The production of bakery products poses great challenges to process control in order to achieve consistent end product quality, as the main components of the products are natural products. The properties of the natural products strongly depend on the parameters during the growth and harvesting of the raw materials as well as their preliminary processes. The production of baked goods involves the product-specific combination of ingredients and the mechanical production of a dough, which is mostly kneaded. Low quality is often the result of incorrect expert assessment of raw material quality and the selected process parameters. A particular problem here is the process transitions or transfers between the process steps of dough preparation, work-up, fermentation phase, pre-baking phase, intermediate storage and post-baking phase. The aim of the project is to increase the product quality of baked goods. This should be achieved by developing a raw material-specific and cross-process production control system that uses artificial intelligence to improve coordination of the production processes while taking into account the specific parameters of the semi-finished products. The improved coordination of the production processes allows the reduction of rejects (resource conservation and traceability) as well as the planning/calculation of achievable product qualities based on quality raw material models to increase the specific process quality.

Duration 01.06.2021 - 31.05.2023, Funded by BMWi

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Projektlogo Test Optimierung mittels  KI-basierter Observer & Simulationen

TOKIOS

Test Optimierung mittels KI-basierter Observer & Simulationen

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TOKIOS aims to integrate innovative methods and techniques from the fields of statistics and artificial intelligence into the integration and system tests of aircraft. The addressed methods are to be applied to offline data, which is of the order of magnitude of big data.

The tasks of the BIBA focus on the development of data integration solutions and the analysis framework in order to be able to set up and use analysis chains in an interoperable manner. Furthermore, the analysis tool is geared towards the needs of the test engineer for future test processes

Duration 01.06.2021 - 31.08.2024, Funded by BMBF,DLR

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