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Digital Quality Analytics Solution for extrusion processes using machine learning and human-oriented dashboards

Project: DAT4.ZERO

Updated at: 21-05-2024

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data processing
Data acquisition
Data analytics
Data modelling
Data storage
Cognitive and artificial intelligence (AI) technologies - machine learning
IoT - Internet of Things
Human Machine Interfaces
Data visualisation

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

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 - 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.

OPTIMAL platform

Project: OPTIMAL

Updated at: 30-01-2024

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Cognitive and artificial intelligence (AI) technologies - machine learning
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