Projects overview
A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context
01-10-2016
-30-09-2019
Included technologies:
- Multimodal interaction mechanisms of workers with automation (voice and gestures).
- Augmented Reality interfaces for assistance and guiding
The A4BLUE framework introduces new ways of interacting with automation mechanisms (specifically, with robots) thorugh natural speaking and gestures.
FIWARE components are used within the A4BLUE architecture (context broker, CEP, identity management, collaborative asset management (FITMAN)).
Robots adapt their behaviour depending on workers' profiles.
A4BLUE enables the transference of knowledge among workers and from the organization to the workers as well, including best practives, instructions, tips from workers, etc., which is integrated within the ICT infrastructure for production.
TWIN-CONTROL | Twin-model based virtual manufacturing for machine tool-process simulation and control
10-01-2015
-30-09-2018
From Waste to Gold | From Waste to Gold , in Swedish: Från spill till guld
01-09-2014
-17-11-2016
COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation
01-09-2016
-31-08-2019
The COMPOSITION project contains a Digital Factory Model Component which offers representation of factory processes and resources in a common format based on well-known standards.
Requirements of modern production processes stress the need for greater agility and flexibility, for faster production cycles, increased productivity, less waste and more sustainable production. Human-machine interaction is put in the center, supporting the decision making process.
Investigation of advanced HMIs for direct interaction with real-world objects. Consideration of mobile user interfaces that allow accessing crucial immediate information everywhere in the factory. Consideration will also be given to data gathering from ultra low power IoT devices such as wireless sensors where data can then be aggregated and visualised at an appropriate HMI interface. Where possible provision for self-powering these IoT devices using energy harvesting will be taken into account in order to avoid battery replacement. Flexible and dynamic data-driven solutions that can be adapted to different environments and needs.
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.
Daedalus | Distributed control and simulAtion platform to support an Ecosystem of DigitAL aUtomation developerS
01-10-2016
-30-09-2019
AUTOWARE | Wireless Autonomous, Reliable and Resilient ProductIon Operation ARchitecture for Cognitive Manufacturing
01-10-2016
-30-09-2019
FIWARE http://www.eclipse.org/app4mc
DIGICOR | Decentralised Agile Coordination Across Supply Chains
01-10-2016
-30-09-2019
DISRUPT | Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration
01-09-2016
-31-08-2019
CloudBoard: offers multiple views and access rights to different human actors Decision Support Toolkit: supports decisions authorised by humans, especially in the shop floor Enterprise and Factory models: accessible and re-configurable through user interaction
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
Some of the functional modules of COROMA taks care off the interaction with humans:
- Avoiding contact when human ooperator is around the robot
- Recognising human gestures to perform specific operation
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
FAR-EDGE | Factory Automation Edge Computing Operating System Reference Implementation
01-10-2016
-31-10-2019
The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric).
We are considering some core GEs from FIWARE as candidate building blocks of our Edge Computing architecture. In particular, the Publish/Subscribe GE (Orion Context Broker implementation) is a good candidate as the northbound interface exposed by Edge Gateways – i.e., computing nodes aggregating a number of local edge nodes (field devices, smart factory equipment) and running local automation and/or analytics processes. We are considering to significantly extend the Publish/Subscribe GE by adding distributed computing capabilities: a data context that is replicated and kept in-sync across a number of GE instances (running anywhere on the network), using a Blockchain and smart contracts as the backing technology. FAR-EDGE will contribute its results, as open source software, to the FIWARE for Industry community.
In the scope of FAR-EDGE, the value of FIWARE is in the OMA NGSI standard: a RESTful Web API implementing the publish/subscribe pattern on context information – i.e., a set of attributes representing the current state of some device or process. NGSI is the common language that FIWARE applications use to integrate themselves with the IoT world. For this reason, supporting NGSI in FAR-EDGE means opening up the Platform to the FIWARE community. The FIWARE asset that is crucial for the support of NGSI is Orion Context Broker (OCB), which as for all FIWARE Generic Enablers is open source software. In FAR-EDGE, we envision the use of OCB to implement the generic publish/subscribe interface of the Distributed Data Analytics subsystem
HUMAN | HUman MANufacturing
01-10-2016
-30-09-2019
ForZDM | Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-stage Manufacturing systems
01-10-2016
-31-03-2021
HIPERLAM | High Performance Laser-based Additive Manufacturing
01-11-2016
-31-01-2020
KRAKEN | Hybrid automated machine integrating concurrent manufacturing processes, increasing the production volume of functional on-demand using high multi-material deposition rates
01-10-2016
-30-09-2019
NIMBLE | Collaboration Network for Industry, Manufacturing, Business and Logistics in Europe
01-10-2016
-31-03-2020
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
MANUWORK | Balancing Human and Automation Levels for the Manufacturing Workplaces of the Future
01-10-2016
-31-03-2020
OpenHybrid | Developing a novel hybrid AM approach which will offer unrivalled flexibility, part quality and productivity
01-10-2016
-30-09-2019
Z-Fact0r | Zero-defect manufacturing strategies towards on-line production management for European factories
01-10-2016
-31-03-2020
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).
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.
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.
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.
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.
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.
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.
vf-OS | Virtual Factory Open Operating System
01-10-2016
-31-10-2019
From a technological point of view, Open vf-OS Platform will provide elements covering the connected world, allowing the exchange and collaboration of information between companies on a value stream thanks to the cloud approach to be adopted (vf-Platform). The Open vf-OS covers from the control device level, where information from the systems (IoT, CPS, embedded systems) is gathered, processed and empowered.
THOMAS | Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems
01-10-2016
-31-03-2021
SAFIRE | Cloud-based Situational Analysis for Factories providing Real-time Reconfiguration Services
01-10-2016
-30-09-2019
Decision support systems
ZAero | Zero-defect manufacturing of composite parts in the aerospace industry
01-10-2016
-30-09-2019
SCALABLE4.0 | Scalable automation for flexible production systems
01-01-2017
-30-06-2020
The A4BLUE framework consists of an ICT infrastructure that enables the integration of automation mechanisms, sensors, legacy systems based on IoT components (FIWARE,...) that enables the assistance and support on decision through divers HMI, including Augmented Reality.