A Practical Method for Detecting Fluff Quality of Fabric Surface Using Optimal Sensing

  • Shoufeng Jin Department of Mechanical Engineering, Xi'an Polytechnic University
  • Qiangqiang Lin Department of Mechanical Engineering, Xi'an Polytechnic University
  • Yu Bie School of Chemical Engineering, Kunming University of Science and Technology
  • Qiurui Ma College of Fashion and Art of Design, Xi'an Polytechnic University
  • Zhixiong Li School of Energy and Power Engineering, Wuhan University of Technology
Keywords: Fabric manufacturing, Machine vision, Artificial intelligence, Optical imaging

Abstract

The raising process has been widely used in manufacturing fabric productions. After raising the surface of the fabric, productions are covered with a fluff layer. The quality of the fabric surface is often valuated by the fluffing type. In order to objectively assess the fluff quality of the fabric surface, an optimal sensing method is proposed in this paper. The fluff contour image was firstly collected by the light-cut imaging device. Then, the fluff region was segmented by the adaptive image segmentation method, the contour coordinates of the fabric were extracted using the freeman chain code and constructed in the form of the binary image. Lastly, a back-propagation neural network (BPNN) was used to learn the relationship between the contour coordinates and the fluff quality. On this basis, a practical fabric fluff detection platform was developed based on the optimal sensing technique. Experimental tests were conducted to evaluate the performance of the proposed method in detecting the fluff quality with four different colours and different fluffing processes. Furthermore, the actual fabric inspection was carried out. The detection correct rate can reach 94.17 %, which can meet the practical production requirement.

Published
2020-02-17
How to Cite
Jin, S., Lin, Q., Bie, Y., Ma, Q., & Li, Z. (2020). A Practical Method for Detecting Fluff Quality of Fabric Surface Using Optimal Sensing. Elektronika Ir Elektrotechnika, 26(1), 58-62. https://doi.org/10.5755/j01.eie.26.1.24221
Section
SYSTEM ENGINEERING, COMPUTER TECHNOLOGY

Funding data