DQ-Meister*in

Digital master craftsperson for quality assessment

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Predictive quality assessment for complex production processes

Predictive Quality Assurance for complex Production Processes
Mastering increasingly complex manufacturing processes is a growing challenge and is further exacerbated by the shortage of skilled workers.

The aim of the project is to counteract this increasing complexity with artificial intelligence (AI) methods and to realise predictive quality assurance in manufacturing and the support of skilled personnel with a digital master (DQ master).
The three main quality assurance tasks are:

a) Process planning
b) Process monitoring
c) Process optimisation

The following AI methods will be used for the three tasks:
a) expert system
b) data-driven digital twins
c) 'eXplainable AI' for root cause analysis related to quality assurance.

The global goal of the project is to create a modular AI-based overall system for predictive quality assessment of complex production processes that can be further developed into a 'DQ-Meister*in'. This will create the basis for the support of the specialists still available in the companies. The complexity of the task demands that sub-goals be defined and pursued in a division of labour. Each of these goals in itself represents progress toward the overall goal.

  • Shortage of skilled workers Mastering ever more complex manufacturing processes is becoming increasingly challenging, especially when faced with the current shortage of skilled labour.
  • Predictive quality assurance and a digital master AI methods are being used to develop predictive quality assurance in the planning, monitoring and optimisation of the production process, as well as a digital master.
  • Support for skilled workers The modular AI-based system for predictive quality assurance in complex production processes can be further developed into a master to support skilled workers.

Project partners

  • Carl Benzinger GmbH
  • Eidgenössische Technische Hochschule (ETH) Zürich
  • Haas Schleifmaschinen GmbH
  • Hahn-Schickard-Gesellschaft für angewandte Forschung e.V.
  • Siemens Aktiengesellschaft
  • GVD Gemeinnützige Vereinigung der Drehteilehersteller e.V.
  • Innovationsnetzwerk SBH
  • TechnologyMountains e. V.

Funding

Funded by the Carl Zeiss Stiftung

 

  • DQ-Meister*in (I58)

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