Industrial Data Services for Quality Control in Smart Manufacturing
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
strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era
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+
AI-Driven Cognitive Robotic Platform for Agile Production environments
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
Context-dependent, Al-based interface for multimodal human-machine interaction with technical logistics systems
Technical logistics systems are becoming increasingly flexible, which means that configuration and control activities of such systems are becoming more complex and require highly qualified IT experts. In contrast, there is a growing shortage of skilled workers in the IT sector. The KoMILo research project aims to counteract this challenge by developing an intuitive and intelligent user interface enabling lower-qualified employees to perform complex tasks on a specialist level. By designing a context-dependent and cross-industry applicable framework that provides the employee process- and situation-dependent suggestions, the project eases the handling of various complex configuration and control operations. Based on basic system functionalities, a digital twin as well as artificial intelligence, interaction and configuration options are generated for the employee by combining and analyzing system, process and employee-related data. In addition to touch input, voice assistants enable multimodal interaction. The framework for the context-dependent, multimodal human-machine interaction is evaluated by using the cellular conveyor system, a driverless transport system as well as a collaborative robotic system.
07.12.2020 - 30.05.2022,
Funded by EFRE: Europäischer Fonds für regionale Entwicklung
Development of a research and technology platform
Research on digitalization and applications of artificial intelligence are advancing rapidly. In spite of excellent results, which are published by publicly funded research projects, there is still a lack of systematic solutions for SMEs that provide robustness and eproducibility of AI and at the same time take the restrictions of software and infrastructure in SMEs into account. The overall aim of the ecoKI project is to close this gap. ecoKI supports SMEs by developing an infrastructure for increasing energy efficiency through AI technologies. ecoKI pursues the following goals:
- Make digitization and AI modules(solutions) more generic in the area of energy efficiency. Moreover, make the available AI and digitalization solutions as easy as possible to use.
- Reduction of barriers that SMEs encounter when they begin to use of digitization and machine learning solutions for increase energy efficiency.
Duration 01.12.2020 - 30.11.2024, Funded by 5. Energieforschungsprogramm
January - July 2021, Bremen
Einstieg in die Datenverarbeitung und -auswertung
11. März 2021, online
Rückverfolgbarkeit von Produkten und Shopfloor Digitalization
25. März 2021, online