Projects overview
MANTIS | Cyber Physical System based Proactive Collaborative Maintenance
01-05-2015
-31-07-2018
QU4LITY | Digital Reality in Zero Defect Manufacturing
01-01-2019
-31-07-2022
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)
There seems to be relationship to predict torque with use of in-line data. Needs to be more explored
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.
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.
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.
Details: AI, foundational, concepts and terminology
Details: AI, Use cases
Details: AI, Framework for AI systems using ML
Details: Industrial IoT standards and roadmapping
Details: IoT RA
Details: Industrial IoT standards and roadmapping
Details: Edge Computing
Details: IoT, interoperability framework
Details: IoT, Vocabulary
Details: IIRA
Details: IoT, Use cases
Details: Big data vocabulary
ZDMP | Zero Defect Manufacturing Platform
01-01-2019
-30-06-2023
Details: Messaging, message Exchange
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT/Device Integration
Details: IoT Architecture
EFPF (European Factory Platform) | European Connected Factory Platform for Agile Manufacturing
01-01-2019
-31-12-2022
DigiPrime | Digital Platform for Circular Economy in Cross-sectorial Sustainable Value Networks
01-01-2020
-31-12-2023
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;
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.
As the proactive exploitation of the DigiPrime platform enables the car-monitored SOH tracing and availability, less testing is needed to assess the residual capacity of the battery. Moreover, by knowing the structure of the battery packs, a decision support system can be implemented to adjust the de-and remanufacturing strategy accordingly and select the most proper cells for re-assembly second-life modules, thus unlocking a systematic circular value chain for Li-ion battery cells re-use. Furthermore, excessively degraded cells which cannot be re-used can be sent to high-value recycling, based on the knowledge of their material compositions.
SHOP4CF | Smart Human Oriented Platform for Connected Factories
01-01-2020
-31-12-2023
AI REGIO | Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs
01-10-2020
-30-09-2023
STAR | Safe and Trusted Human Centric Artificial Intelligence in Future Manufacturing Lines
01-01-2021
-31-12-2023
COALA | COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence
01-10-2020
-30-09-2023
COALA is a solution for cognitive assistance that consists of a composition of trustworthy AI components with a voice-enabled digital intelligent assistant as an interface.
COALA will contribute in the ongoing discussion about AI ethics and potential standards, monitor the standardization potential for worker education under consideration of AI competencies, and will use and contribute to IT standards. We will take into account available IT-related standards and use them when applicable. This includes normative standards as set by ISO and its national bodies, which are of high importance to industrial companies. We will assess standards proposed by major influential de-facto standardization bodies like W3C, OASIS, and OMG. Standardization topics concern:
- The technical system architecture level. Standardize communication protocols, data exchange formats, data exchange interfaces.
- The content level. Ontologies for the semantic data integration and the WHY engine.
- The process level. This includes innovation management system, terminology, terms and definitions, and tools and methods.
ASSISTANT | leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments
01-11-2020
-31-10-2023
DAT4.ZERO | Data Reliability and Digitally-enhanced Quality Management for Zero Defect Manufacturing in Smart Factories and Ecosystems
01-10-2020
-31-03-2024
RFID Positioning System – Use of multiple RFID receivers within the cell would allow for 3D location tracking of the parts to be assembled in the system, ensuring parts are correctly present and in the right locations before proceeding.
User interfaces were designed in WinCC
RFID Positioning System – Use of multiple RFID receivers within the cell would allow for 3D location tracking of the parts to be assembled in the system, ensuring parts are correctly present and in the right locations before proceeding.
User interfaces were designed in WinCC
Grafana – Used for the creation of bespoke data visualisation solutions.
Python / Tensorflow / Pytorch – Used for the creation of bespoke machine learning algorithms and analysis processes.
Grafana – Used for the creation of bespoke data visualisation solutions.
Python / Tensorflow / Pytorch – Used for the creation of bespoke machine learning algorithms and analysis processes.
BOOST 4.0 | Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
01-01-2018
-31-12-2020
OPTIMAL | Automated Maskless Laser Lithography Platform for First Time Right Mixed Scale Patterning
01-10-2022
-30-09-2026
5G-TIMBER | Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector
01-06-2022
-31-05-2025
KYKLOS 4.0 | An Advanced Circular and Agile Manufacturing Ecosystem based on rapid reconfigurable manufacturing process and individualized consumer preferences
01-01-2020
-31-12-2023
AI-PROFICIENT | Artificial Intelligence for improved PROduction efFICIEncy, quality and maiNTenance
01-11-2020
-31-10-2023
The proposed approach is underpinned by predictive and prescriptive AI analytics at both component and system level, by cross-fertilizing edge and platform AI, while leveraging the human knowledge and feedback for reinforcement learning (human-in-the-loop)
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.