Projektlogo Konzeption und Erforschung einer digitalen und KI-unterstützten, flexiblen Montagelinie zur Steigerung von Produkt- und Prozessinnovation

EKIMuPP

Design of a digital and AI-assisted flexible assembly line to increase product and process innovation

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Innovation cycles are becoming increasingly shorter at the product and process level, making rapid integration into assembly a critical success factor for companies. The EKIMuPP research project aims to specifically increase the level of innovation by means of the integration of digital and AI-based systems. The project is dedicated to the development of an innovation-oriented assembly system that increases and optimizes the interaction of employees along the entire process chain through digital interaction capabilities. In addition, context-based training and support of operational employees is enabled by an assistance system. AI-based variant management will also be developed to manage complexity. The overall system will be developed in a modular fashion and its benefits will be demonstrated and evaluated in practice. For this purpose, typical products for manual assembly will be selected and the overall system will be evaluated in the company context. Furthermore, the functionalities of the overall system will be evaluated in a practical test with further product variants in order to prove its product-independent applicability.

Duration 01.03.2021 - 30.06.2022, Funded by EFRE: Europäischer Fonds für regionale Entwicklung
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MARS

Mobile Autonomous Robot for Safe sorting

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Motivation

Automated guided vehicles or autonomous mobile robots are widely used for sortation applications in postal and e-commerce applications. In this context, they increase both flexibility and scalability of logistics sorting centers. Currently, two contrasting solutions are present in the market. First, collaborative sorting robots that operate at low speed in the same areas as humans. This allows employees to load and unload objects from the robots. These systems are characterized by very high flexibility. However, for safety reasons, they operate at reduced speed and thus lower performance. Second, non-collaborative systems that operate completely separate from humans, allowing higher speeds and performance. For this, however, they require a cage that severely restricts potential usage scenarios.

Goal

The MARS project will develop an intelligent system that allows switching between these two principles. Based on an existing vehicle, it offers the optimal compromise between the two modes of operation; it is safe for humans and at the same time offers maximum possible efficiency through increased driving speed in closed-off areas.

Procedure

The system is equipped with a unique and secure localization of the position in each vehicle. Based on the determined position, the vehicles independently adjust their behavior. In collaborative areas, they intelligently avoid people, vehicles and other obstacles. If the vehicles detect that they are in a cordoned-off area, they increase their operating speed, maximizing the performance of the system.

This solution allows any combination of collaborative workspaces with areas of maximum performance. Accordingly, the MARS system can be used both to expand existing systems and to design new flexible sorting systems.

Duration 01.01.2021 - 31.12.2021, Funded by EIT Manufacturing

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Projektlogo Datendienste für die Qualitätskontrolle in Industrie 4.0

i4Q

Industrial Data Services for Quality Control in Smart Manufacturing

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i4Q will provide a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) able to manage the huge amount of industrial data coming from cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework will guarantee data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimisation; based on a microservice-oriented architecture for the end users. With i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zerodefect manufacturing.

i4Q Solutions will efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology. i4Q Project will provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.

BIBA focuses on: 1) the creation of data quality guidelines for manufacturing and 2) the extension of its software QualiExplore to support a) production data quality knowledge, and b) production line certification under the aspect of data quality. The extension of QualiExplore includes the integration of a digital assistant (conversational AI).

Duration 01.01.2021 - 31.12.2023, Funded by H2020

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Projektlogo strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era
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ENHANCE

strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era

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ENHANCE aims at strengthening the cooperation between 3 EU and 4 PC universities across recent research outcomes related to MPQ 4.0. From a capacity-building perspective, this consortium will improve the capacity of HEI in PC with innovative programmes. It will develop new competencies and skills to transfer later to socio-economic partners. ENHANCE will guarantee the sustainability of the consolidated learning programs and materials through the creation of 2 new DIH in PC.

Duration 01.01.2021 - 31.12.2023, Funded by Erasmus+

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Projektlogo KI-gesteuerte kognitive Roboterplattform für agile Produktionsumgebungen
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ACROBA

AI-Driven Cognitive Robotic Platform for Agile Production environments

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ACROBA project aims to develop and demonstrate a novel concept of cognitive robotic platforms based on a modular approach able to be smoothly adapted to virtually any industrial scenario applying agile manufacturing principles. The novel industrial platform will be based on the concept of plug-and-produce, featuring a modular and scalable architecture which will allow the connection of robotic systems with enhanced cognitive capabilities to deal with cyber-physical systems (CPS) in fast-changing production environments. ACROBA Platform will take advantage of artificial intelligence and cognitive modules to meet personalisation requirements and enhance mass product customisation through advanced robotic systems capable of self-adapting to the different production needs. A novel ecosystem will be built as a result of this project, enabling the fast and economic deployment of advanced robotic solutions in agile manufacturing industrial lines, especially industrial SMEs. The characteristics of the ACROBA platform will allow its cost-effective integration and smooth adoption by diverse industrial scenarios to realise their true industrialisation within agile production environments. The platform will depart from the COPRA-AP reference architecture for the design of a novel generic module-based platform easily configurable and adaptable to virtually any manufacturing line. This platform will be provided with a decentralized ROS node-based structure to enhance its modularity. ACROBA Platform will definitely serve as a cost-effective solution for a wide range of industrial sectors, both inside the consortium as well as additional industrial sectors that will be addressed in the future. The Project approach will be demonstrated by means of five industrial large-scale real pilots, Additionally, the Platform will be tested through twelve dedicated hackathons and two Open calls for technology transfer experiments.

Duration 01.01.2021 - 30.06.2024, Funded by H2020

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