Project: DAT4.ZERO
Updated at: 21-05-2024
Project: Productive4.0
Updated at: 29-04-2024
Project: IMPROVE
Updated at: 29-04-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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: OPTIMAL
Updated at: 30-01-2024
Project: AI REGIO
Updated at: 04-07-2023
Project: Fortissimo 2
Updated at: 03-10-2022
Project: BEinCPPS
Updated at: 29-09-2022
Updated at: 26-03-2021
Project: QU4LITY
Updated at: 28-07-2020
Project: TWIN-CONTROL
Updated at: 13-02-2019
Project: TWIN-CONTROL
Updated at: 13-02-2019
Project: Fortissimo 2
Updated at: 23-06-2018
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.