AI REGIO | Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs
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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.
01-10-2017
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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.
01-09-2017
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UPTIME platform focusses on the use of condition monitoring techniques, e.g. event monitoring and data processing systems, that will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data and to handle the real-time data in complex, dynamics environement in order to get meaningful insights and to decide and act ahead of time to resolve problems before they appear, e.g. to avoid or mitigate the impact of a future failure, in a proactive manner. Moreover, UPTIME proposed unified framework will not be limited to monitoring and diagnosis but it aims to cover the whole prognostic lifecycle from signal processing and diagnostics till prognostics and maintenance decision making along with their interactions with quality management, production planning and logistics decisions.
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Robotized Assembly Process Using Dual Arm Robot. https://www.sciencedirect.com/science/article/pii/S2212827114011354
01-10-2016
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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.
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Several functional modules of COROMA system allows it to interact with the production envionment in an enhanced way:
In some of the use case applications, the interaction with the workpiece:
01-10-2016
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Robots adapt their behaviour depending on workers' profiles.
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