Designing Predictive Tools for Personalized Functionalities in Knitted Performance Wear

Studio Eva x Carola

Abstract

Developments of advanced textile manufacturing techniques—such as 3D body-forming knitwear machinery—allows the production of almost finalized garments, which require little to no further production steps to finalize the garment. Moreover, advanced knitting technology in combination with new materials enables the integration of localized functionalities within a garment on a ‘stitch by stitch level.’ There is potential in enhancing the design tools for advanced knitting manufacturing through the use of technologies such as data gathering, machine learning, and simulation. This approach reflects the potential of Industry 4.0, as design, product development, and manufacturing are moving closer together. However, there is still limited knowledge at present about how these new technologies and tools can have an impact on the creative design process. The case study presented in this paper explores the potential of predictive software design tools for fashion designers who are developing personalized advanced functionalities in textile products. The main research question explored in this article is: ``How can designers benefit from intelligent design software for the manufacturing of advanced personalized functionalities in textile products?''. Within this larger research question three sub-research questions are explored: (1) What kind of advanced functionalities can be considered for the personalization process of knitwear? (2) How to design interactions and interfaces that use intelligent predictive algorithms to stimulate creativity during the fashion design process? (3) How will predictive software impact the manufacturing process for other stakeholders and production steps? These questions are investigated through the analysis of a Research Through Design case study, in which several predictive algorithms were compared and implemented in a user interface that would aid knitwear designers during the development process of high-performance running tights.

Publication
Temes de disseny, 2019, Num. 35, pp. 42-75
RMSE and R2 values for the Weighta target. The multiple colored lines represent the prediction results for a common cross-validation holdout set. Both RMSE and R2 suggest that some models (like Boosted Trees and Neural Network) can fit the data very well.
RMSE and R2 values for the Weighta target. The multiple colored lines represent the prediction results for a common cross-validation holdout set. Both RMSE and R2 suggest that some models (like Boosted Trees and Neural Network) can fit the data very well.
Screenshot of the working prototype. The control area in the left side allows the designer to change the proportion of special knit type and structure by dragging the line left and right. During the manipulation the values on the right will give direct feedback about the predicted feedback. By changing the algorithm in the drop-down list different predictions can be tested. The statistics give feedback about the reliability of the prediction.
Screenshot of the working prototype. The control area in the left side allows the designer to change the proportion of special knit type and structure by dragging the line left and right. During the manipulation the values on the right will give direct feedback about the predicted feedback. By changing the algorithm in the drop-down list different predictions can be tested. The statistics give feedback about the reliability of the prediction.
Martijn ten Bhömer
Martijn ten Bhömer
Assistant Professor of Product Design & Manufacture

Specialized in the design, research and development of intelligent products.

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