SCOTT | Secure COnnected Trustable Things
01-05-2017
-31-10-2020
Projects overview
Showing 241 to 270 of 467 results
XS2I4MS | Access to I4MS
01-09-2015
-28-02-2018
BEinCPPS | Business Experiments in Cyber Physical Production Systems
11-01-2015
-31-10-2018
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Technologies and enablers
Result items:
Cloud level: I2Lab
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Cloud level: Test Back-end
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Field level: Controller/Actuator (HW), TestExecutor (SW)
Factory level: FIWARE IDAS Gateway for CPPS
The data acquisition is performed by the monitoring core, performed in C. The BBB + Mikrobus + Thermoclicks provide access to the data gathered by the sensors.
Sensors:
-Temperature: MIKROELEKTRONIKA Thermocouple Type-K Glass Braid Insulated
-Pressure: KISTLER 6190C
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The Field/Factory levels feed normalized streams of raw data to the Cloud level for further processing.
Historical analysis of the production process data, through data analysis, will enable to establish trends of the mold usage.
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(offline): MSEE BIM Toolbox, UAModeler
Usage of the UA-Modeler tool to model the OPC-UA address space, containing the variables describing the acquired data.
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Cloud level: FIWARE Context Broker + Cygnus extension
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Cloud level: I2Lab
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Factory level: Rule Editor (Android app), Test Front-End (Android app)
Data visualization based on FIWARE Wirecloud widgets
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Cloud level: Cygnus extension
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The device platform supports a Linux distribution.
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The monitoring core has been developed in C.
The OPC-UA layer has been developed in Node.js
HORSE | Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes
11-01-2015
-31-07-2020
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Technologies and enablers
Comment: HORSE adopts the IoT paradigm and OSGI middleware enabling and facilitating remote control and monitoring of the production line and the available resources and ease installation. For that, the robot controllers, sensors and human-robot- interfaces will be coherently and seamlessly integrated as controllable and active agents in the production. Feedback from agents and sensors will be collected, analysed and used for process control decisions involving the dynamic deployment of cobotic, robotic, and human agents. Thus the constituted advanced closed loop production process control system integrates the high-level, state-of-the-art production process control and the low-level sensor measurements and high-tech robot and cobot solutions and cobot actuation control.
Comment:
HORSE includes human-machine interaction with Interfaces targeted to be easy-to-use by unskilled workers and learning by demonstration capabilities, to be able to program a robot without the need for robotics expertise.
Comment: HORSE encompasses multi-modal supervision and control modes for a variety of existing and novel robotic co-workers: cooperative robots (cobots) and “third hand” robots for diverse manufacturing applications, and also demonstration based robot programming techniques for an intuitive programming of robots tasks by non-robotics experts.
Comment:
HORSE also includes Augmenter Reality applications, with intuitive instructions and alerts for various manufacturing tasks.
Comment: HORSE will develop an integrated, process-oriented management model for the high-level control of the production line process with online, automatic allocation of resources (robots, human workers, interaction components, etc.) and rapid and straightforward dynamic re-allocation as necessary considering: the real-time monitoring of the production chain process; the actual worker safety and condition in the execution of the process; the robot positions within the autonomous mobile platforms; the required collaboration between human workers and robots and the available interaction modalities; the automated management of exceptions, occurring as effect of safety breaches, material or agent unavailability, delays in previous steps, e.g. triggered by quality checks, etc.
Comment: HORSE incorporates the design and development of an innovative hybrid position/force control for intrinsically safe flexible robots.
NIMBLE | Collaboration Network for Industry, Manufacturing, Business and Logistics in Europe
01-10-2016
-31-03-2020
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Technologies and enablers
Comment:
Applied Technologies:
Spring Boo: Spring Boot is a framework for building web applications. It is built on top of the Spring Framework and follows a zero-configuration principle. The major set of microservices are build using Spring Boot as an application framework.
Spring Cloud: Functionalities for building and integrating microservices are provided by Spring Cloud. It mainly aggregates components of the Netflix Open Source Software (Netflix OSS) project and makes them easily be integrated with
Spring Boot applications. Components of the underlying microservice infrastructure are heavily using modules from Spring Cloud (e.g. Service Discovery, Configuration Server and Gateway Proxy).
Spring Cloud Security: Standardized security mechanisms are implemented using Spring Cloud Security. It provides out-of-the-box integration of security modules to Spring Cloud applications. Authentication and authorization between microservices are realized by using Spring Cloud Security, which supports OAuth2 and OpenID Connect and communicates with the authentication server (i.e. Cloud Foundry UAA).
ELK Stack Logs can be streamed to Logstash, which stores them persistently in Elastic Search. visualizations are done using Kibana, hence the ELK stack. The ELK stack is used to aggregate log output of distributed microservices in order to centrally perform analysis of generated log output.
Cloud Foundry UAA: The Cloud Foundry User Account and Authentication (UAA) is a multi tenant identity management service, available as a stand alone OAuth2 server issuing tokens for clients. Cloud Foundry UAA acts as identity and authentication server issuing OpenID Connect tokens.
Camunda BPM: Camunda BPM is an open source platform for business process management. Camunda BPM is used for the definition and execution of business processes (e.g. supply chain process).
Apache Marmotta: Apache Marmotta is an open implementation of a linked data platform. Apache Marmotta will be mainly used to store catalog data and perform product-search queries. Apache Solr Apache Solr is a free-text indexing tool providing advanced search and navigation capabilities on the indexed data. Apache Marmotta uses Apache Solr for its semantic search cores composed semantic features of indexed items.
Docker: Docker is an open-source solution for application deployment, consisting of prebuilt images running inside a container. Docker will be used for intermediate development releases and on-premises deployment.
PostgreSQL: PostgreSQL is an open-source database system for object-relational data. PostgreSQL will mainly be used as a database technology, in order to have a homogeneous setup.
Apache Kafka: Open Source messaging infrastructure Mainly used for private communication among components and entities.
Data management:
- Product life-cycle management
- Web objects for IoT data ingestion
Comment: Open source big data capable (Apache) projects
Result items:
ReconCell | A Reconfigurable robot workCell for fast set-up of automated assembly processes in SMEs
11-01-2015
-28-02-2019
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Technologies and enablers
INEDIT | open INnovation Ecosystems for Do It Together process
01-10-2019
-31-03-2023
FOCUS | Factory of the Future Clusters
01-01-2015
-31-12-2016
GO0D MAN | aGent Oriented Zero Defect Multi-stage mANufacturing
01-10-2016
-30-09-2019
BOOST 4.0 | Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
01-01-2018
-31-12-2020
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Technologies and enablers
MANTIS | Cyber Physical System based Proactive Collaborative Maintenance
01-05-2015
-31-07-2018
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Technologies and enablers
Comment:
One of the objectives of the MANTIS project is to design and develop the human-machine interface (HMI) to deal with the intelligent optimisation of the production processes through the monitoring and management of its components. MANTIS HMI should allow intelligent, context-aware human-machine interaction by providing the right information, in the right modality and in the best way for users when needed. To achieve this goal, the user interface should be highly personalised and adapted to each specific user or user role. Since MANTIS comprises eleven distinct use cases, the design of such HMI presents a great challenge. Any unification of the HMI design may impose the constraints that could result in the HMI with a poor usability.
RADICLE | Real-time dynamic control system for laser welding
01-02-2015
-31-10-2018
Z-Fact0r | Zero-defect manufacturing strategies towards on-line production management for European factories
01-10-2016
-31-03-2020
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Technologies and enablers
Comment:
Within the Z-Fact0r, the proposed (higher level) DSS, with the support of the knowledge base and the online inspection module (1st level decision support at single stage), produce, verify and validate decisions aligned with the quality control policies, production targets, desired product specifications and maintenance management requirements. Key functional characteristics of the envisioned DSS incorporates among others, techniques for monitoring and predicting product quality, action prioritization, root cause analysis, and mitigation planning algorithms (at product and workstation level). Moving beyond existing solutions that focus only on specific aspects of the production procedure, or that are restrained to diagnosis, the proposed DSS system incorporates autonomous, hierarchical decision support, based on process analytical technologies and newly developed suitably adjusted knowledge-based systems, and combines product monitoring models and data analytics from heterogeneous sources. The envisioned DSS takes into account a wide set of multiple factors and criteria, such as data uncertainty, lack of information and information quality, involvement of multiple actors, and real-time response. Thanks to the 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-REPAIR and Z-MANAGE) the overall solution presents a significant contribution to a spectacular improvement in the overall performance and reliability of the targeted multi-stage manufacturing systems and in the production agility (response to continuous adjustments in production targets).
Comment:
DATAPIXEL provides the information associated to the defect detection in the manufacturing parts selected. This information is used as an input for developing the defect detection algorithms of Z-Fact0r solution. Based on this input, a data conditioning methodology has been developed to extract information concerning to the defect position and type. This information will be used as baseline for the model validation, via comparison with the respective simulation results.
The procedure that has been used is the following:
- Image-based feature extraction: Convolutional Neural Networks (CNN) and Variational Auto Encoders (VAE) used as feature extractors. CNNs will define the appropriate features that have been used for the classification between healthy and defected parts, and VAEs will be utilized to distinguish that can be used for image generation.
- Feature selection: An efficient filter feature selection (FS) method was developed for selecting informative and non- redundant feature subsets. In addition to enhanced accuracy rates and dimensionality reduction, the method have reasonably low computational demands. A robust and computationally efficient evaluation criterion with respect to patterns was defined allowing us to assess the redundancy between the features. The proposed FS technique was performed on a forward selection basis handling simultaneously both the discrimination power and the complementary characteristics between the extracted features. To decide on the number of retained features, a termination condition was finally introduced, thus avoiding the trial-and-error procedure usually employed in the common FS techniques of the literature.
- Classification: The selected features were input in a virtual classification module. The role of this module is to provide a decision on the workpiece condition. The technologies used were Artificial Neural Networks (ANN) and deep learning algorithms.
Comment:
Nowadays, it is familiar that within the Industry 4.0, the ICT and the CPS, as parts of the industrial processes, are implemented and merged. For data collection, sensors are being used, imbedded within the AI in order to make smooth communication among humans and machines. Thus, Z-Fact0r is a pioneer with several advances in predictive maintenance, IoT sensors on shop floors and endless communication between the various components of the system, creating effective and many efficient applications for Industry 4.0.
Comment:
Once a “repairable” defect is detected (Z-DETECT), a proper and customized repairing action must be deployed with the minimum time and effort, assuring the best productivity and production flow. In fact, a major challenge for an effective ZD manufacturing is related with the capability to automatically repair the occurred defects without perturbing the overall production flow.
Z-Fact0r is developing a model-based, supervisory control solution that will be able to interpret the interstage quality control measurements together with the monitoring of the process itself, in order to identify the defect sources and generate a proper and customized repairing action. Additive manufacturing in the form of inkjet or paste printing of various materials (metal, ceramic, polymer resins) can successfully be used to fill a missing spot or correct a damaged part. Upon detection of the defected area, the printing head will deliver the patch material in solution or paste form. In the case of inkjet printing, defect as small as 20 μm can be patched. Post printing treatment of the delivered material include solvent evaporation (e.g. in the case of polymer patches), UV curing (e.g. in the case of epoxy resins) and low temperature laser sintering in the case of metal or ceramic nanoparticles, thermal curable resins or paste where a local reflow process is required.
To facilitate the implementation of the five strategies, Z-Fact0r is supporting a “reverse supply-chain” policy in the context of a multi-stage supply-chain attached to a multi-stage production. As a result, the defected products/parts detected in downstream stages (produced during a stage, or provided from suppliers in a particular stage) could be returned to upstream stages for remanufacturing or recycling.
Comment:
Additive manufacturing (AM) is a widely used set of techniques used to build objects by adding layer-upon-layer of material. While materials typically used are plastic, metal or concrete, nowadays AM technologies are expanding to include all kind of materials such as ceramic, nanocomposites, glass, and other.
In Z-Fact0r, we exploited AM-based technologies as a tool for repairing of components in a production line. Thanks to the ability for local deposition, i.e. precision placement of material at desired position, AM was the optimum choice to correct or repair a defect. Moreover, AM combined with subtracted manufacturing techniques for the effective repairing. In context, in the case of a defect, material can be subtracted by means of laser ablation or classical machining, thus removing of the problematic area cleaning or preparing the surface. Then, AM is used to fill the defect with the desired material. A final step of sintering or other processing used to finalize the repairing action.
Result items:
Additive manufacturing (AM) is a widely used set of techniques used to build objects by adding layer-upon-layer of material. While materials typically used are plastic, metal or concrete, nowadays AM technologies are expanding to include all kind of materials such as ceramic, nanocomposites, glass, and other.
In Z-Fact0r, we exploited AM-based technologies as a tool for repairing of components in a production line. Thanks to the ability for local deposition, i.e. precision placement of material at desired position, AM was the optimum choice to correct or repair a defect. Moreover, AM combined with subtracted manufacturing techniques for the effective repairing. In context, in the case of a defect, material can be subtracted by means of laser ablation or classical machining, thus removing of the problematic area cleaning or preparing the surface. Then, AM is used to fill the defect with the desired material. A final step of sintering or other processing used to finalize the repairing action.
Comment:
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.
Comment:
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.
ToMax | Toolless Manufacturing of Complex Structures
01-01-2015
-31-12-2017
MOTOR | Multi-ObjecTive design Optimization of fluid eneRgy machines
09-01-2015
-31-08-2018
ENCOMPASS | ENgineering COMPASS
01-10-2016
-29-02-2020
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Technologies and enablers
JOIN-EM | JOINing of copper to aluminium by ElectroMagnetic fields
01-09-2015
-31-08-2018
DELPHI4LED | From Measurements to Standardized Multi-Domain Compact Models of LEDs
01-06-2016
-30-09-2019
Eco-Solar | Eco-Solar Factory - 40%plus eco-efficiency gains in the photovoltaic value chain with minimised resource and energy consumption by closed loop systems
10-01-2015
-30-09-2018
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Technologies and enablers
Comment: Eco-Solar project web site: http://ecosolar.eu.com/
FineSol | Assembly of miniaturized PCBs by using low cost hyper-fine solder powders
11-01-2015
-30-04-2019
MAShES | Multimodal spectrAl control of laSer processing with cognitivE abilities
01-12-2014
-30-11-2017
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REProMag | Resource Efficient Production Route for Rare Earth Magnets
01-01-2015
-31-12-2017