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TexBoost – less Commodities more Specialities

Centro de Computação Gráfica

TexBoost – less Commodities more Specialities

2017-2020

The TexBoost – less Commodities more Specialties mobilizing project is a structuring project of the Textile Cluster: Technology and Fashion.

Its objective is to cluster collective R & D initiatives with a strong inductor and demonstrator component, involving Textile and Clothing companies, as well as other complementary sectors of the economy.

Activities

This project has as main activities:

  •  developing new textile solutions, with the creation of new scientific knowledge;
  •  to develop very innovative products and processes using state-of-the-art technologies that will provide new experiences for consumers and participating businesses;
  •  simplify access by the companies involved in new markets/business areas or reinforce existing markets/business areas, nationally and internationally.

Work phases

The TexBoost project is organized in 6 PPS – Products, Processes, and Services – built from 50 activities, resulting in 17 innovative solutions in several areas.

  •  PPS 1 – Digitization and dematerialization
  •  PPS 2 – New materials
  •  PPS 3 – New structures
  •  PPS 4 – IE Textiles
  •  PPS 5 – Circular Economy
  •  PPS 6 – Management (management, dissemination and exploitation of results)

The contribution of the CCG

The CCG – Centre for Computer Graphics – is involved in the PPS phase 1 of the project, more specifically in nuclear activity 1, which includes the technological development in the scope of digitization and dematerialization of tissue samples.

The applied research domain EPMQ of the CCG is responsible for the research and development of a computer tool for dematerialization of tissue samples that will comprise the conceptualization and implementation of a digital platform for integration, treatment, and analysis of decision support data in the virtual prototyping of fabrics.

For this purpose, we will use approaches applied to the development of interoperability architectures and BigData and Machine Learning algorithms. These algorithms are able to learn from the history of textile data, as well as to predict and optimize variables/parameters useful for decision-making processes in textile production.

Project website

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