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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

Lombardy Champion - Zero-Hours Quality

Project: BEinCPPS

Updated at: 29-09-2022

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Comment: Cloud level: I2Lab
Data processing
Comment: Cloud level: Test Back-end
Data acquisition
Comment: Field level: Controller/Actuator (HW), TestExecutor (SW) Factory level: FIWARE IDAS Gateway for CPPS
Data analytics
Comment: The Field/Factory levels feed normalized streams of raw data to the Cloud level for further processing.
Data modelling
Comment: (offline): MSEE BIM Toolbox, UAModeler
Data storage
Comment: Cloud level: FIWARE Context Broker + Cygnus extension
Cognitive and artificial intelligence (AI) technologies - machine learning
Comment: Cloud level: I2Lab

Rhône-Alpes Champion: The Smart Mold

Project: BEinCPPS

Updated at: 29-09-2022

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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data acquisition
Comment: 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
Data analytics
Comment: Historical analysis of the production process data, through data analysis, will enable to establish trends of the mold usage.
Data modelling
Comment: Usage of the UA-Modeler tool to model the OPC-UA address space, containing the variables describing the acquired data.

Pilot - Batteries

Project: DigiPrime

Updated at: 03-02-2022

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

Operational services aim to collect product data on post-use Li-Ion batteries about their use phase in order to enable monitoring and full traceability of its life-cycle;

Data analytics
Comment:

Operational services aim to:

  • elaborate and analyse various data collected about post-use batteries to predict the conditions of the battery packs, modules and cells;
  • define a Decision Support System to identify the best disassembly and remanufacturing strategy, given the post-use Li-Ion battery conditions.
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