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
01-01-2020
-30-06-2023
Social-manufacturing platform that enables multi-stakeholder interactions and collaborations to support user-driven open-innovation and co-creation.
Social-manufacturing platform that enables multi-stakeholder interactions and collaborations to support user-driven open-innovation and co-creation.
01-01-2019
-31-07-2022
01-10-2018
-31-03-2022
The Virtual Design Tool (CORE component of the ROSSINI solution) wants to ease the design process of a HR cell implementation (e.g., helping with the sensor placing or the hazard assessment evaluation)
01-10-2017
-30-09-2021
CloudiFacturing will extend the field of action of the technology developed in CloudFlow and CloudSME from the product development process to the production process, in order to leverage factory data with analytics algorithms and simulation tools
Thanks to cloud resources, enough power computing is available to analyze different scenarios in a few days instead of several weeks.
Designers of CATMARINE and SKA are now able to achieve high-quality products by analyzing different manufacturing scenarios without wasting time, money and material.
The platform is able to optimize the resin injections points/vents and verify the presence of defects in the final product, thus ensuring a complete and correct mold-filling.
Outcomes of the project creates base for the improvement of the existing design of the water quench and will be used for the development of the new generation of the nozzles.
It is expected that new nozzle design and thus new water quench will be available for the customers in 5 years time. It is expected that those new products will attract new clients: 5 new contracts in 1 year increasing to 10 new contracts in 5 years, which will increase the turnover of Ferram by 500k Euros in 1 year and 3,5 million Euros in 5 years after the experiment end.
01-10-2017
-31-03-2021
The modelling and simulation methods used in Z-BRE4K are mainly Finite Element Methods (FEM) where complex problems and processes from the real world are being simplified and solved using a numerical approach. First, an accurate digital model of the geometry and material properties of all involved objects, boundary conditions between these objects and process data is created (i.e. forces or temperature).
Then, the complex shape of all objects involved, is approximate using a finite number of simple geometries (i.e. triangles) which simplify the complex mathematical problem. A computer is capable of solving these mathematical operations at a rate impossible for humans and thus enables the user to analyse various scenarios, ranging from mechanical strains within the objects to rise in temperature or material fatigue. This information can be used to predict the remaining useful lifetime of a given tool.
Simulation platform is deployed by the physical equipment to create intuitive maintenance control and management systems. The Z-BRE4K’s platform simulation capabilities will estimate the remaining useful life calling for maintenance and suggesting the optimal times to place orders for spare parts, reducing the related costs. The increased predictability of the system and the failure prevention actions will reduce the number of failures, maximise the performance, reduce the repair/recover times reducing further the costs.
By applying time series analysis, we are able to detect special events that are known (Fault detection) or unknown (anomaly detection) during production. This information, correlated with sensor readings is fed into machine learning algorithms that create estimates of Remaining Useful Life (RUL), Health Indexes (HI) and forecast upcoming events (Likelihood of Failure). Special focus is given in techniques that can provide real-time information (Fast computation and high accuracy) as well as being scalable in order to use new data as it becomes available. Additional information such as meantime between failures based on historical data or an expert opinion, CAE data, quality control data, real time states etc. are also used to the design of machine simulators.
01-11-2017
-28-02-2021
01-10-2017
-31-03-2021
01-10-2017
-31-03-2021
Digital models enahnced with real world data acquired from sensor devices will be used as the basis of physical phenomena that affect the operational condition of the equipment, such as degradation. THis will result in the improvement of the accuracy of the predictive maintenance functionalities of the SERENA platfrom and tools.
01-10-2016
-31-03-2021
01-01-2015
-31-12-2017
FALCON aims to provide methodologies and approaches for representation of design knowledge on the one hand and additionally for forecasting and simulation mechanism. The representation of design knowledge will be based on existing standards and the developments dedicated to the Rule Interchange Format, which has been developed within the FP7 LinkedDesign project. Furthermore, FALCON supports the optimization of Forecasting and Simulation mechanism. By using real-life data gathered from Product Embedded Information Devices, sensors or from social media (blogs, twitter, facebook etc.) throughout the whole lifecycle optimized results are expected in order to find the best solutions for new generation product-services.
The FALCON Open Virtual Platform aims to support different domains dedicated to the whole lifecycle of products. In order to achieve this goal the Virtual Open Platform will include an ontology enabling different domains to exploit the FALCON VOP. Dedicated to the field of new product design the Rule Interchange Format will be further developed in order to represent design knowledge in a neutral format. This way also general valid knowledge (such as Moment of Inertia etc.) can be re-used in different contexts of product design.
01-10-2022
-30-09-2026
01-12-2014
-30-11-2018
01-04-2015
-31-03-2019
01-10-2016
-31-03-2021
10-01-2015
-30-09-2018
01-01-2015
-31-12-2018
11-01-2015
-31-12-2018
09-01-2015
-31-08-2018
Optimization algorithms can calculate better plant configurations. Thesting these new configurations in a real plant can be very cost intensive as production may be compromised during configuration and testing. A virtual environment which is able to simulate new parameters and verify them is a big deal as the running production is not compromised during testing and evaluation of new parameters.
The main focus of IMPROVE are learned models. Manual modeling of system models is not suitable for the complex, fast chaning industrial plants we have today. Lots of expert knowlege is needed to manually create a model. Learned models can be created using only data and little to no expert knowledge is required depending on the technology.
01-01-2017
-30-06-2020
01-01-2015
-31-12-2017
01-01-2015
-31-12-2017
01-10-2016
-31-10-2019
01-01-2015
-30-06-2018
01-01-2015
-31-12-2017
01-09-2016
-31-08-2019
09-01-2015
-31-08-2018
CAxMan addresses Computer Aided Technologies of Additive Manufacturing and Complete workflows, including Design and Simulation, providing models aimed at product life-cycle
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-09-2016
-30-11-2019