ProRegio | Customer-driven design of product-services and production networks to adapt to regional market requirements
01-01-2015
-31-12-2017
01-01-2015
-31-12-2017
01-01-2015
-31-12-2017
01-01-2015
-30-06-2018
01-01-2015
-31-12-2017
11-01-2015
-31-10-2018
01-09-2015
-31-08-2018
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
12-01-2015
-31-05-2019
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.
10-01-2015
-30-09-2018
10-01-2015
-30-09-2018
11-01-2015
-31-10-2018
01-09-2016
-31-08-2019
A Simulation and Forecasting Toolkit analyses the production processes and required resources in an integrated way and extracts forecasts for possible failures. Also forecasting is provided in supply chain and logistics, especially in fill level monitoring of bins and boosts the waste management and recycling processes. Sustainable manufacturing will be assisted by a Decision Support System. The Marketplace will enable dynamic integration with actors in the supply chain.
01-10-2016
-30-09-2019
01-09-2016
-31-08-2019
01-09-2016
-30-11-2019
01-10-2016
-29-02-2020
01-10-2016
-30-09-2019
01-10-2016
-31-10-2019
01-10-2016
-31-03-2021
01-10-2016
-31-03-2021
01-10-2016
-30-09-2019
01-01-2017
-30-06-2020
01-11-2015
-31-10-2017
Cyper Physical Production System and digital twins requires data collection from real system
01-11-2017
-28-02-2021
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.
01-10-2017
-31-03-2021
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.