Zero Defect Manufacturing

Consortium partner

GKN


Further information at http://www.forzdmproject.eu


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723698.

Manufacturers face the challenge of continuously operating their manufacturing processes and systems to deliver the required production rates of high quality products with increasing complexity and limited resources.

"Zero Defect Manufacturing" (ZDM) is a new paradigm that goes beyond traditional Six Sigma approaches in high-technology and strategic European manufacturing areas through new knowledge-based approaches.

The ZDM paradigm is central to the management of production quality objectives in advanced manufacturing enterprises. The implementation of this paradigm in industry requires innovative fault management and control methods, new technologies for the inspection and integration of knowledge management and IKT tools, for intelligent and sustainable decisions in complex industrial scenarios that are not available on the market.

Ziel des ForZDM-Projektes ist die Entwicklung und Demonstration von Werkzeugen zur Unterstützung des schnellen Einsatzes von ZDM-Lösungen in der Industrie und dem Design von wettbewerbsfähigeren und robusteren mehrstufigen Fertigungssystemen.

The proposed ZDM approach is based on the combined application of new knowledge-based data acquisition and problem-solving analyses to reduce the generation of defects, as well as new on-line defect management and improved production traceability solutions to reduce the spread of defects along production stages. This is achieved through the proper integration of innovative key technologies such as cyber physical systems, selective inspection, advanced analytics and integrated process and partial flow control solutions.

ABF - Industrial Automation is a member of the consortium of the ForZDM project, which is funded by the EU's Horizon 2020 Research and Development Programme. ABF brings 30 years of experience as an automation specialist to Work Packages 2-4 and researches standards and best practice approaches for knowledge-based data and root cause analysis to improve fault prevention in multi-stage manufacturing operations.