There seems to be relationship to predict torque with use of in-line data. Needs to be more explored
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.
Project: Fortissimo 2
Updated at: 03-10-2022
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.
Updated at: 06-10-2021
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)
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.
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.
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.
Updated at: 08-08-2019
Updated at: 22-06-2018