In-Line Quality Control System for the Industrial Production of Multiaxial Non-crimp Fabrics

Marcel Haeske (), Bahoz Abbas, Tobias Fuertjes and Thomas Gries
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Marcel Haeske: RWTH Aachen University, ITA
Bahoz Abbas: IMA/ZLW & IfU, RWTH Aachen University
Tobias Fuertjes: RWTH Aachen University, WZL
Thomas Gries: RWTH Aachen University, ITA

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2015/2016, 2016, pp 699-707 from Springer

Abstract: Abstract A new approach is presented in which existing systems for error detection are used to build up a closed loop control for quality optimization in the production process of NCF. Only with this online system, productivity and product quality can be increased. The fibre material is screened for fibre disorientation and gaps and deviations in areal weight at different positions alongside the production line using state of the art equipment for error detection. Additionally, the machine-parameters are monitored and the yarn tension of both reinforcement fibres and stitching yarn are measured continuously. An automated and self-learning control is developed that automatically adjusts the settings of the production machine. By this, a minimum of errors in the textile is achieved. In addition, the quantitative description generated in the process is used to develop an error map for each batch of material produced. This error map allows for production waste to be minimized, as the produced material can be automatically sorted into different quality grades, meeting the demands of different customers. Furthermore, the material cutting can be adjusted that only low quality areas are removed. By this, only high quality material will be used for the component part production. An entire roll of non-crimp fabric would not be declared as waste. The quality control is realized by an automatic control device. By a pressure roll in front of the warp knitting unit the rovings are homogenized.

Keywords: Non-Crimp Fabric; Quality; Production; Pressure Rol (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-42620-4_53

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