CloudiFacturing | Cloudification of Production Engineering for Predictive Digital Manufacturing
01-10-2017
-31-03-2021
01-10-2017
-31-03-2021
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.
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).
01-10-2022
-30-09-2026
01-01-2015
-31-12-2017
01-01-2015
-01-01-2018
01-10-2012
-30-09-2015
01-01-2015
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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.
01-10-2016
-30-09-2019
01-11-2011
-30-04-2015
01-11-2011
-31-10-2014
01-11-2011
-31-10-2014
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
10-01-2015
-31-12-2018
12-01-2015
-31-05-2019
01-04-2015
-01-04-2019
09-01-2015
-31-08-2018
01-09-2015
-31-08-2018
01-07-2013
-31-12-2016
01-09-2013
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01-09-2013
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01-11-2013
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01-09-2013
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01-09-2013
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01-10-2013
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01-06-2013
-31-05-2016
01-09-2013
-31-08-2016
01-09-2013
-31-08-2016
01-09-2013
-31-08-2016
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.