PROGRAMS | PROGnostics based Reliability Analysis for Maintenance Scheduling
01-10-2017
-31-03-2021
01-10-2017
-31-03-2021
01-05-2015
-31-07-2018
01-06-2017
-31-05-2020
01-06-2018
-31-05-2021
01-06-2018
-31-05-2021
01-05-2017
-31-10-2020
01-05-2018
-31-10-2021
01-06-2016
-30-09-2019
01-07-2015
-31-07-2018
01-01-2018
-01-01-2021
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.
Milling Digital Twin enables strategy design and quality control in milling processes with only SW tools, simulation and virtual optimisation
Cockpit optimiser software provides environment for intelligent design of an automated cell with the customer.
Cockpit optimiser and Milling Digital Twin with AI tools for accelerating current design and optimisation processes by operators
Solutions to facilitate the analytical thinking of the operator. The solution will help the operator with the correlation of quality and process parameters in order to make a decision upwards in the process.
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.
There is a need of managing large Data Sets and Big Data, IA solutions for different Manufacturing Processes. Solutions need to support operators in decision-making
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.
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.
Complete machine parameters correlation is realized, allowing machine operators to take into account all the assets from each workstation of the production line. It enhances its capacity in relation to conventional analytics methods.
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.
01-01-2019
-30-06-2023
01-01-2019
-31-12-2022
01-09-2017
-29-02-2020
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:
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.
01-10-2019
-30-09-2023
01-01-2020
-31-12-2023
01-10-2019
-31-03-2023
01-01-2020
-30-06-2023
Social-manufacturing platform that enables multi-stakeholder interactions and collaborations to support user-driven open-innovation and co-creation.
Social-manufacturing platform that enables multi-stakeholder interactions and collaborations to support user-driven open-innovation and co-creation.
01-09-2019
-30-11-2022
01-10-2019
-31-03-2024
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.