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: INCLUSIVE
Updated at: 29-04-2024
Project: PERFoRM
Updated at: 29-04-2024
Project: A4BLUE Factory2Fit HUMAN INCLUSIVE MANUWORK
Updated at: 20-03-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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.
Project: QU4LITY
Updated at: 01-02-2024
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.
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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.
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: COALA
Updated at: 16-01-2024
Project: A4BLUE
Updated at: 22-12-2023
Project: Factory2Fit
Updated at: 05-10-2022
Project: Factory2Fit
Updated at: 05-10-2022
Project: Factory2Fit
Updated at: 04-10-2022
Project: Factory2Fit
Updated at: 04-10-2022
Project: Factory2Fit
Updated at: 04-10-2022
Project: INCLUSIVE
Updated at: 04-10-2022
Project: INCLUSIVE
Updated at: 04-10-2022
Project: HUMAN
Updated at: 04-10-2022
Project: HUMAN
Updated at: 04-10-2022
Project: Factory2Fit
Updated at: 04-10-2022
Project: HUMAN
Updated at: 04-10-2022
Project: Factory2Fit
Updated at: 04-10-2022
Project: INCLUSIVE
Updated at: 04-10-2022
Project: INCLUSIVE
Updated at: 04-10-2022
Project: MANUWORK
Updated at: 04-10-2022
Project: MANUWORK
Updated at: 04-10-2022
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.