AI REGIO | Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs
01-10-2020
-30-09-2023
01-10-2020
-30-09-2023
01-11-2019
-30-04-2023
01-10-2017
-31-03-2021
Manufacturing SMEs are empowered to compute and solve problems that cannot be tackled without Cloud and HPC technology, making them more competitive by reducing development times for innovative products with better performance.
01-10-2017
-31-03-2021
In particular, the project aims the development of intelligent and predictive maintenance systems for the new manufacturing trends of mass customisation and individualisation. Increased reliability of production systems is considered to be crucial for securing competitive advantage for manufacturing companies. At present, maintenance in general and predictive maintenance strategies in particular are facing significant challenges in dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. So, preventive maintenance strategies designed for traditional highly repetitive and stable mass production processes based on predefined components and machine behaviour models are no longer valid and more adaptive and responsive (predictive-prescriptive) maintenance strategies are needed.
Z-BRE4K will provide a modular solution for predictive maintenance that is highly customizable. Therefore, the different modules of the solution cane be sold as stand alone products or can be combined depending on the users needs. Z-BRE4K solution can be applied to both new machines and old machines that were not designed to be equipped with predictive maintenance solutions.
01-09-2017
-31-08-2020
UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. The UPTIME solution will be applicable to any production system incorporating sensors and will be based on real-time reliability-related (prognostic) information in order to reduce the equipment downtime and malfunctions with the aim to produce high-quality products with optimized losses. It will utilize sensors for measuring various parameters of the production process, provide diagnostic outcomes, i.e. the current equipment health state, generate predictions about future equipment behaviour, and recommend optimal actions at optimal times. It will also incorporate a continuous improvement mechanism for continuous learning of Diagnosis, Prognosis and Maintenance Decision Making phases triggered by sensor data during maintenance and other operational actions implementation. The elimination of unexpected failures will lead to an increased level of safety in the workplace and to improved overall operations efficiency.
UPTIME will be able to be applied in the context of the production process of any manufacturing company regardless their processes, products and physical model used. It will take advantage of predictive maintenance management, industrial IoT and big data, as well as proactive computing and the e-maintenance concept in order to reframe predictive maintenance strategy and to create a unified information system in alignment to the new predictive maintenance strategy framework and to Gartner’s 4 levels of data analytics maturity.
UPTIME will be applicable at the level of component, machine and production system, depending on the placement of sensors throughout the production lifecycle and the data availability in the manufacturing company’s systems (e.g. Enterprise Resources Planning- ERP, Manufacturing Execution System- MES). Within UPTIME, there will be interactions between the various e-maintenance services and the e-operations data and information from the manufacturing companies’ systems in order to synchronise maintenance with production, quality and logistics management. The results of the UPTIME solution will be evaluated by the manufacturing companies participating in the consortium and will be demonstrated in manufacturing companies beyond the consortium.
UPTIME aims to deliver novel e-maintenance services and tools to support the daily work of maintenance engineers as well as the overall maintenance management with the aim to optimize in-service efficiency. UPTIME solution consists of extended e-maintenance services and tool, which will incorporate novel methods and algorithms for addressing the phases of the UPTIME framework and conclude in a novel predictive maintenance solution covering the whole prognostic lifecycle.
The UPTIME solution will combine and extend existing predictive maintenance tools and services (USG/BIBA, preIno/BIBA, PANDDA /ICCS, SeaBAR/Pumacy, and DRIFT/RINA-C) and will define the way for its implementation in a systematic and unified way with the aim to fully exploit the advancements in ICT and maintenance management by examining the potential of big data in an e-maintenance infrastructure.
Extended version of USG will implement the Signal Processing phase; extended version of preInO will address the Diagnosis and the Prognosis phases; extended version of PANDDA will deal with Maintenance Decision Making; extended version of SeaBAR will address the Maintenance and Operational Actions Implementation; and extended version of DRIFT will deal with data-driven FMECA.
Each extended UPTIME service will incorporate real-time data-driven information processing technologies and algorithms so that the integrated system is able to cover complete scenarios and fulfil the needs of the manufacturing companies participating in the consortium. The aforementioned tools will interact when necessary to the manufacturing company’s system (e.g. ERP, MES) in order to exchange data and information for scheduling production, quality and logistics activities together with maintenance activities (e.g. by using the production plan of the ERP system). Through the Continuous Improvement mechanism, UPTIME will be able to continuous learn with the aim to update and improve Diagnosis (Detect), Prognosis (Predict) and Maintenance Decision Making (Decide) phases by gathering actions-related and/ or failure-related sensor data (Act).
Through its unique bundle of a predictive maintenance management framework and an integrated platform, UPTIME will prevent critical asset failure, deploy resources more cost-effectively, maximize equipment uptime and enhance operations, while accelerating the quality and supply chain processes. By better diagnosing, anticipating and acting upon the asset performance and product quality, the UPTIME novel predictive maintenance framework is expected to increase in-service efficiency by at least 10% in the 3 pilot cases through combined KPIs measurements.
By investigating and demonstrating the applicability of the UPTIME predictive maintenance system in three pilot cases in different manufacturing sectors, UPTIME will contribute to a more widespread adoption of predictive maintenance and demonstrate more accurate, secure and trustworthy techniques at component, machine and system level.
01-10-2016
-30-09-2020
01-01-2015
-01-01-2018
01-01-2015
-01-01-2018
01-10-2012
-30-09-2015
Flexible human-like dual arm robots in cooperation with human workers ultimately aims to enable manufacturing of highly customised products, which is the main objective of the project.
01-01-2015
-01-01-2018
01-09-2016
-31-08-2019
01-10-2016
-30-10-2019
01-10-2016
-30-09-2019
COROMA copes witha a series of applications where workpiece position, size and even flexibility are limitations for a successful automatised operation of the robotic system that performs the manufactiuring process.
Aerospace, Energy and Naval sectors give the project a wide range of scenarios where the robotic system must show adaptability: automatic finishing grinding of metalic surfaces, thin walls machining, grinding of complex metalic rack weldings, sanding of composite workpieces whose position must be previously localized, or nozzel inspection are the some examples of the demanding tasks COROMA must fface.
Regarding adaptability, the system is able to generate the operation trajectories needed for each complex workpiece, and to learn along the execution of each operation.
01-09-2012
-31-08-2016
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-30-09-2020
01-11-2011
-30-04-2015
01-11-2011
-31-10-2014
01-11-2011
-31-10-2014
01-10-2016
-30-09-2019
01-10-2016
-31-03-2020
MANUWORK Augmentede Reality based information distribution system will support the operators/workers with delivering work instructions at the working station. This will help operators coping with high product variance and customization
02-09-2013
-01-09-2016
01-01-2015
-01-01-2019
01-01-2017
-30-06-2020
01-10-2016
-30-09-2019
01-11-2016
-31-10-2019
Printed Electronics is the key step for fully digitizing electronics manufacturing, thus being able to customize the product without making changes in the infrastructures or process.
01-09-2015
-31-10-2019
Optimize production processes and producibility using Cloud/HPC-based modelling and simulation, leveraging online factury data and advanced data analytics, thus contributing to the competitiveness and resource efficiency of manufacturing SMEs.