journal article·2007-12

Optimization of the needle punching process for the nonwoven fabrics with multiple quality characteristics by grey-based taguchi method

Chung-Feng Jeffrey Kuo, Te-Li Su, Cheng-Ping Tsai

Fibers and Polymers· Volume 8· Issue 6
Publisher page

Abstract

This study is intended for finding out the optimal processing parameters for needle punching nonwoven fabrics in order to work out its maximal strength. Taguchi method together with grey relational analysis is employed to resolve the problem as regards multiple-quality optimization, and further discover the optimal combination of processing parameters for needle punching nonwoven fabrics. Firstly, orthogonal array L18(21×37) is used to deal with the processing parameters that may exert influence over the manufacturing of needle punching nonwoven fabrics. Then grey relational analysis is applied to resolve the deficiency of Taguchi method that focus on single quality characteristic. Next, the response table of grey relational analysis is used to obtain the optimal combination of processing parameters for multiple quality characteristics. In the current experiment quality characteristic refers to the tensile strength and tear strength of the nonwoven fabrics. Additionally, signal-to-noise ratio (SN ratio) calculation and analysis of variance (ANOVA) can be adopted to explore the experimental results. Through ANOVA, the significant factors that exert comparatively significant influence over the quality characteristic of the needle punching nonwoven fabrics, that is, the control factors are determined so that the quality characteristic of the needle punching nonwoven fabrics can be effectively controlled. Finally, confirmation experiment is conducted within 95 % confidence interval to verify the experimental reliability and reproducibility.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Textile materials and evaluations · Industrial Vision Systems and Defect Detection · Color Science and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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