Increasing globalization, shorter product and technology cycles, ever-changing end-user requirements and more varying market dynamics are leading to the need to make quick, effective and affordable changes to the production process. This requires advanced production machines with the ability to be quickly and affordably adapted to new product designs and production materials. This leads to developments in the flexible production process, which should enable first-time-right production.

How can flexible, first-time-right manufacturing be enabled through the application of models to predict manufacturing process parameters through technology development?

Start project: 01-01-2017                         End project: 31-12-2020

The development of models to predict process parameters is an innovation that leads to faster and more efficient production. The following benefits can be listed here:

  • The production process for 1-series will be cheaper;
  • The time-to-market is getting shorter, for both 1-piece and mass production.
  • 1 pcs and mass products are produced first-time-right;
  • It may be a greater variety of products from one production machine;
  • The quality of 1-series becomes manageable.

The partners in this project want to express this worldwide trend in two production machines and remain at the forefront of developments in this technology within the region. That is why DAM, Bunova, Bond and VMI have joined forces to build up knowledge more quickly and effectively around the theme of modeling process parameters in order to be able to produce more efficiently, faster, more flexibly and qualitatively. Fundamental knowledge and the valorisation of this knowledge are indispensable here.

The companies DAM, Bunova, Bond and VMI run for diverse application domains (3D printer for high-performance materials, rubber extrusion in tire building machines) against the same problem: unexplained inaccuracy in production results because process parameters are not be famous. By sharing knowledge and experiences between these companies from Gelderland and Overijssel and a joint development together with the UT and Saxion knowledge institutions, it is expected that research into and development of new techniques to create and apply models to predict process parameters for production is accelerated.

Partners

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Saxion

Hogeschool in Enschede, Deventer en Apeldoorn

www.saxion.nl
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Universiteit Twente

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Europees Fonds voor Regionale Ontwikkeling

Dit project is mede mogelijk gemaakt door de Europese Unie en het Europees Fonds voor Regionale Ontwikkeling

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