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FAR-EDGE Architecture and Components Specifications

Project: FAR-EDGE

Updated at: 29-04-2024

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Technologies and enablers
Information and communication technologies
Programming Frameworks – Software Development Kits (SDKs)
FIWARE
Smart Industry Context Information Management and Persistence
The Orion Context Broker Generic Enabler
Comment:

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

Techniques for product reworking

Project: Z-Fact0r

Updated at: 29-04-2024

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Technologies and enablers
Advanced material processing technologies
Additive manufacturing
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. 

Pilot - WHR Dryer Factory Holistic Quality Platform

Project: QU4LITY

Updated at: 01-02-2024

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Technologies and enablers
Information and communication technologies
Data spaces
Engineering tools
System modelling - digital twins, simulation
Knowledge-workers and operators
Smart and Analytical operator
Comment:

The end2end process supported by the overall architecture helps the operator and team leader in their daily activities in order to prevent and anticipate as much as possible quality issues on the product via the analysis of a huge amount of data linked together via the holistic semantic model.

Pilot - SIEMENS SIMATIC Products Quality Improvements

Project: QU4LITY

Updated at: 01-02-2024

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data processing
Data acquisition
Remaining challenge
Comment:

Through innovative algorithms and statistical methods, possible data sources for predictive quality control can be identified and evaluated. Moreover, by cooperation of all project partners, the realization of data access and acquisition along the whole process chain can be realized. With a focus on algorithms and methodology, a use case-specific algorithm is going to be implemented and validated to maintain high prediction accuracy.

Data availability is a challenge: Limited access to measurement data (due to limited access to third-party systems)

Data analytics
Comment:

By applying sophisticated algorithms and methods on the acquired data, systematic failure root cause detection supported by data analytics can be implemented. In addition, improved knowledge of machine states/maintenance requirements for neuralgic points can be implemented through the desired solution path within this pilot.

Cognitive and artificial intelligence (AI) technologies - machine learning
Comment:

For this trial, the acquired test data will be analyzed regarding quality classification. In every test a part could pass or fail. Failed parts must be reworked, if possible, and brought back to the process. Sometimes parts are classified as failed even if they are good (false positive). This effect will be analyzed by machine learning algorithms and, if necessary, adopted in classification parameterisation. Additionally, the fact of 100% testing, means every panel is tested automatically, with bottleneck in out of the line test stations will be addressed in setting up failure prediction models for quality forecast. This will be supported by data analysis of pre reflow AOI (automated optical inspection). 

With all these data analysis and process optimization activities economical evaluation will be included to support decisions in-process and configuration changes. For the development of these applications, the main steps are data availability/access, data processing, and model development. The developed applications should be deployed on Edge devices.

Pilot - RiaStone Autonomous Quality ZDM for “Ceramic tableware Single-firing”

Project: QU4LITY

Updated at: 01-02-2024

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Technologies and enablers
Knowledge-workers and operators
Smart and Analytical operator
Comment:

The ZDM-Autononous Quality Solutions are used as systems that perform tasks in an autonomous/automated way, requiring the intervention of an operator only when an operational tie-breaker is needed.  When that is the case, the operator has to analyse the incident and provide for a solution to the AQL System, interacting with it via an HMI interface.

Pilot - PHILIPS OneBlade shaving unit production line

Project: QU4LITY

Updated at: 01-02-2024

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data analytics
Remaining gap
Comment:

There seems to be relationship to predict torque with use of in-line data. Needs to be more explored

Prescriptive analytics
To be
Descriptive analytics
As is
Predictive analytics
To be
Diagnostic analytics
As is
Cognitive and artificial intelligence (AI) technologies - machine learning
Comment:

An AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development.

Knowledge-workers and operators
Smart and Analytical operator
Skills requirement
Comment:

Enable operators to work in a more complex environment while reducing the strain of administrative tasks and enabling easy production analytics by capturing information online instead of on paper. 

Shopfloor worker (operator – technical support group): From a shopfloor perspective new job profiles, or altered job profiles should be defined, however In essence the job profiles will remain the same, while the operators and Technical Support Groups need to understand & be able to work with these new technologies. This requires some basic knowledge on the (digitalized) systems, for the operators a lot can be captured in SOP’s (Standard Operating Procedures), but the technical support staff should also have some basic knowledge on the workings and the hardware/software side of the systems in order to be able to support the shopfloor where needed.

Pilot - KOL’s Real-time injection moulding process monitoring-control

Project: QU4LITY

Updated at: 01-02-2024

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Technologies and enablers
Knowledge-workers and operators
Smart and Analytical operator
Skills requirement
Comment:

With the help of skilled production line workers, the data in the AI platform can be annotated and herewith produce the predictive models for ZDM autonomous quality inspection. The platform gives users the ability to monitor the AQ process (Autonomous Quality) and provide feedback for the ZDM.

To acquire quality data, all involved users and managers must understand some basic data science principles. Machine vision in modern times relies on large amount of consistent data. Data acquisition process begins with organized collection of samples, which should become an integral part of every standardized manufacturing process that involves automated quality inspection or ZDM.

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