Projects overview
SYMPLEXITY | Symbiotic Human-Robot Solutions for Complex Surface Finishing Operations
01-01-2015
-31-12-2018
ambliFibre | adaptive model-based Control for laser-assisted Fibre-reinforced tape winding
01-09-2015
-31-08-2018
HORSE | Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes
11-01-2015
-31-07-2020
IMPROVE | Innovative Modeling Approaches for Production Systems to raise validatable efficiency
09-01-2015
-31-08-2018
Optimization algorithms can find sub-optimal configurations of the plant and improve them. Newly calculated system parameters are verified in a simulation before they are applied to the real plant.
MC-SUITE | ICT Powered Machining Software Suite
10-01-2015
-30-09-2018
ReconCell | A Reconfigurable robot workCell for fast set-up of automated assembly processes in SMEs
11-01-2015
-28-02-2019
COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation
01-09-2016
-31-08-2019
A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context
01-10-2016
-30-09-2019
Robots adapt their behaviour depending on workers' profiles.
Daedalus | Distributed control and simulAtion platform to support an Ecosystem of DigitAL aUtomation developerS
01-10-2016
-30-09-2019
ConnectedFactories | Industrial scenarios for connected factories
01-09-2016
-30-11-2019
COROMA | Cognitively enhanced robot for flexible manufacturing of metal and composite parts
01-10-2016
-30-09-2019
Several functional modules of COROMA system allows it to interact with the production envionment in an enhanced way:
- Moving trough the workshop when required
- Sensing and recognicing the work environment and the wokpiece
- Avoiding collision with humans, machines and pieces
- Adapting the robot movements to the orientation of the workpiece
- Learning from previous experiences for performance improving
- Communicating and cooperating whit machine tools in a sichronised way
In some of the use case applications, the interaction with the workpiece:
- Is made by using the robot as a mobile support, controling the force applien on the workpiece
- Is made with specific tools adapted in the project: sanding tools, mechatronic hand
ENCOMPASS | ENgineering COMPASS
01-10-2016
-29-02-2020
Factory2Fit | Empowering and participatory adaptation of factory automation to fit for workers
01-10-2016
-30-09-2019
INCLUSIVE | Smart and adaptive interfaces for INCLUSIVE work environment
01-10-2016
-30-09-2019
HUMAN | HUman MANufacturing
01-10-2016
-30-09-2019
MANUWORK | Balancing Human and Automation Levels for the Manufacturing Workplaces of the Future
01-10-2016
-31-03-2020
Z-Fact0r | Zero-defect manufacturing strategies towards on-line production management for European factories
01-10-2016
-31-03-2020
Z-DETECT is the first strategy of the Z-Fact0r solution: the detection strategy consists of detecting any machining process anomaly or instability through process monitoring by means of controlled variables called critical process variables (CPVs). In particular, this strategy is invoked when a defect is being generated after the adaptation of the parameters. In such a scenario, an alarm is being triggered to flag the parameters that resulted in a defect. By mapping the true reasons, the system will be able to avoid having more generated defects by weighting the system model.
Apart from the inspection of the product from which the defect is being observed, the strategy involves more actions and processes to deal both with the generation of the detected defect, and its propagation to the next stages.
Z-PREDICT strategy is triggered when a defect is recognised during the Z-DETECT stage. The events detected from the physical layer of the system are engineered into high value data that will stipulate new and more accurate process models. Such an unbiased systems behaviour monitoring and analysis provides the basis for enriching the existing knowledge of the system (experience) learning new patterns, raising attention towards behaviour that cause operational and functional discrepancies (e.g. alarms) and the general trends in the shop-floor.
The more the data pool is being increased the more precise (repeatability) and accurate the predictions will be. The estimations for the future states involve the whole production line, e.g. machine status after x number of operations and/or quality of the products for given set of parameters.
The system will predict with high confidence the expected quality and customer satisfaction, allowing modifications to the parameters before the production of the products. In addition, Z-Fact0r can operate in the reverse mode, i.e. insert a Customer Satisfaction Goal and control the parameters accordingly to achieve this target.
The ability of Z-Fact0r to optimise the manufacturing processes according to certain/target quality levels and/or customer satisfaction is the key innovation to fulfil the industrial requirements.
The overall supervision and optimisation of the system is achieved after the execution of Z-MANAGE strategy. The defects are processed with Decision support system (DSS) tools and are interfaced with Manufacturing Execution Systems (MES). False positives and false negatives are clustered after each Z-Fact0r strategy, which results into a good filtering of these false alarms. To achieve so, the previous acquired knowledge and incidents are also processed to fine tune the system’s operation.
Additionally, the production is optimised by better scheduling, taking into account the environmental impact of each process. The optimised scheduling and adaptability of the manufacturing improves the overall flexibility, placing a premium on the production rates, satisfying the demand, while preserve increased machinery availability. Since, the Knowledge management system will tune the whole production according to certain quality levels and customer satisfaction, it is highly anticipated that the overall performance of the system will suffice the increased needs of the customers.
Z-Manage strategy involves also a Knowledge based decision support system which collects knowledge from all the components and the operators and therefore is able to suggest solution for the tuning the rest of the components.
The strategy involves also the decision making in the event of a defect. The defect will be analysed via the inspection system, from which the defect can be classified and categorised on its severity. In case of “repairable” defects the system will decide for the following; (i) rework on spot, (ii) removal from the production line for further inspection and rework. If the defect is classified as “non-repairable” then the system will decide whether (a) the product will be forwarded to upstream stages, or (b) considered as total failure where it will be recycled.
ZAero | Zero-defect manufacturing of composite parts in the aerospace industry
01-10-2016
-30-09-2019
PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-28-02-2021
L4MS | Logistics for Manufacturing SMEs
01-10-2017
-31-03-2021
Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories
01-10-2017
-31-03-2021
Z-break will make it possible to combine the current manufacturing systems with current and new mechatronic systems. These combinations will lead to smarter manufacturing systems and thus a shorter ramp up in generating higher quality and productivity.
Part of the Z-BRE4K project is the development, of a novel embedded condition monitoring solution with cognitive capabilities, by applying deep learning techniques to reduce the dimensionality of multimodal sensor data associated to a given machine/device, and provide meaningful features to predictive maintenance services on the cloud. Most suitable IoT edge devices, for optimal trade-off between computational power and energy consumption, sensors, providing relevant information of the condition of different components, and signal processing algorithm are proposed for different machines and processes. Data gathering is enabled by the installation of IoT gateways, where data in different protocols are homogenised and sent to the cloud for storage. Real-time data, relevant KPIs and information about components status are visualised through dedicated dashboards.
ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing
01-10-2018
-31-03-2022
The ROSSINI modular KIT offers advance components for the different layers of a robotic application (sensing, perception, cognition, control, actuation and integration)
The RS4 Controller gather and fuse data from different sensor sources. Within ROSSINI the following sensor sources have been developed as EXTRA components able to be connected with the RS4 controller: 3D Vision cameras, Lidar arrays, Radars and Skins.
The ROSSINI Controller (CORE component of the ROSSINI Modular KIT) integrates a Semantic Scena Map, a Flexible layer (Scheduler) and an Execution layer (Motion Planner) to guarantee optimal efficiency of the robot (also considering Job Quality factors)
Within the ROSSINI project an advanced collaborative robot have been developed, equipped with advanced and novel interfaces, and able to perfom very low breaking time.
All the three ROSSINI demonstrators (white goods, electronic equipment, and food packaging) proof the feasibility and the advantages of ROSSINI Platform Implementations in relevant (diffenet and complex) industrial environments
Human-Robot Collaboration is key in the development of the Rossini solution: all the use cases look at this scenario, allowing the human operation working in the same cell with the robot, on the same machine and even on the same work-piece.