Analysis and Construction of a Quality Prediction System for Needle-Punched Non-woven Fabrics

• Published in: Fibers and polymers 2007-02, Vol.8 (1), p.66-71
• Main Authors: Chung Feng Jeffrey Kuo, Te Li Su, Chin Hsun Chiu, Cheng Ping Tsai
• Format: Article
• Language: Korean
• Subjects: Cross-Lapper; Needle punching machine; Neural network; Taguchi method; Tensile strength
• ISSN: 1229-9197; 1875-0052

In this study, polyester and polypropylene staple fibers were selected as the raw material, and then processed through roller-carder, cross-lapper and needle-punching machine to produce needle-punched non-woven fabrics. First, the experiment was planned using the Taguchi method to select processing parameters that affect the quality of the needle-punched non-woven fabric to act as the control factors for this experiment. The quality characteristics were the longitudinal and transverse tensile strength of the non-woven fabric as well as longitudinal and transverse tear strength. The $L_{18}(2^1{\times}3^7)$ orthogonal array was selected for the experiment as it offered an improvement on the traditional method that wastes a lot of time, effort and cost. By using the analysis of variance(ANOVA) technique at the same time, the effect of significant factors on the production process of needle-punched non-woven fabrics could be determined. Finally, the processing parameters were set as the input parameters of a back-propagation neural network(BPNN). The BPNN consists of an input layer, a hidden layer and an output layer where the longitudinal/transverse tensile and tear strength of the non-woven fabric were set as the output parameters. This was used to construct a quality prediction system for needle-punched non-woven fabrics. The experimental results indicated that the prediction system implemented in this study provided accurate predictions.