QU4LITY mapped on
Knowledge-workers and operators

General desciption of Knowledge-workers and operators:

The European Factories of the Future are expected to provide global manufacturing competitiveness, but also to create a large amount of work opportunities for the European population. Future factory workers are therefore key resources for industrial competitiveness as well as important consumers. However, the changing demographics and high skill requirements faced by European industry pose new challenges. Workers with high knowledge and skills (“knowledge workers”) will be scarce resources.

The classification shown below has been successfully applied under the Ace factories cluster projects (A4BLUE, Factory2Fit, HUMAN, INCLUSIVE, and MANUWORK) funded under the horizon 2020 call “FOF-04-2016: Continuous adaptation of work environments with changing levels of automation in evolving production systems”.  More details can be found in The ACE factories White paper - Human-centred factories from theory to industrial practice. Lessons learned and recommendations.

  • Augmented and Virtual Worker
  • Social worker - operator
  • Collaborative worker (Human-Robot cooperation)
  • Super-strong worker
  • Health and happy operator
  • Smarter and Analytical operator

 

Relevant:

  • Results:

    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.

    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 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.

    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

     

    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.

    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 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.

    Cockpit optimiser and Milling Digital Twin with AI tools for accelerating current design and optimisation processes by operators