Digital Quality Analytics Solution for extrusion processes using machine learning and human-oriented dashboards
Project: DAT4.ZERO
Updated at: 21-05-2024
Project: DAT4.ZERO
Updated at: 21-05-2024
Project: Productive4.0
Updated at: 29-04-2024
Project: vf-OS
Updated at: 29-04-2024
Project: COMPOSITION
Updated at: 29-04-2024
Project: BOOST 4.0
Updated at: 29-04-2024
Project: BOOST 4.0
Updated at: 29-04-2024
Project: Z-Fact0r
Updated at: 29-04-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
There seems to be relationship to predict torque with use of in-line data. Needs to be more explored
Project: COALA
Updated at: 16-01-2024
Project: COALA
Updated at: 31-07-2023
Project: AI REGIO
Updated at: 04-07-2023
Project: BEinCPPS
Updated at: 29-09-2022
Project: BEinCPPS
Updated at: 29-09-2022
Updated at: 09-08-2022
Project: DigiPrime
Updated at: 03-02-2022
Operational services aim to:
Project: SAFIRE
Updated at: 04-06-2021
Project: SAFIRE
Updated at: 22-03-2021
Project: SAFIRE
Updated at: 22-03-2021
Project: QU4LITY
Updated at: 28-07-2020
Project: TWIN-CONTROL
Updated at: 13-02-2019
Project: TWIN-CONTROL
Updated at: 13-02-2019
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.