Analysis and construction of a quality prediction system for needle-punched non-woven fabrics

Kuo, Chung‐Feng Jeffrey; Su, Te‐Li; Chiu, Chin-Hsun; Tsai, Cheng-Ping

doi:10.1007/bf02908161

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…BPNN was also called generalized delta (į) rule, which is nonlinear extension of į algorithm, its basic principle is to feedback error generated at output layer to hidden layer via weight, so as to correct hidden layer weight. BPNN used the gradient steepest descent approach to minimize error function [8][9][10][11][12].…”

Section: B Wavelet Transformmentioning

confidence: 99%

Automatic recognition of textile texture using back-propagation neural network

Su¹,

Hong²,

Chang³

et al. 2011

2011 3rd International Conference on Computer Research and Development

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This study proposed to use wavelet transfer to acquire image features, and use back-propagation neural network to classify type of textile texture. Firstly, wavelet transfer is applied to obtain vertical, horizontal and diagonal images of original image, and compute its wavelet energy to take them as texture features of this image. Finally, the backpropagation neural network is adopted to recognize texture feature of this image. As indicated by experimental result, this system can recognize accurately texture in woven fabric. Among 350 test samples in total, the general recognition rate amounts to 96%. Therefore, this study succeeded in building the automatic computer visual inspection system to recognize textile texture type, which can greatly improve and avoid current low efficiency, non-objective judgment and labor waste due to human inspection.

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Section: B Wavelet Transformmentioning

confidence: 99%

Automatic recognition of textile texture using back-propagation neural network

Su¹,

Hong²,

Chang³

et al. 2011

2011 3rd International Conference on Computer Research and Development

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show abstract

“…To address the above problem, this study aimed to design a fabric surface softness testing system. It first devised the orthogonal array according to the Taguchi method, [1][2][3][4][5] and set the testing conditions based on the experimental plan designed by the orthogonal array. The researchers cooperated technologically with the industry to conduct peach finishing in a factory based on the Taguchi Experiment Method.…”

Section: Introductionmentioning

confidence: 99%

Design and verification of fabric surface softness testing system

Kuo

Lin

Su³

2011

Textile Research Journal

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This study developed a testing system for fabric surface softness based on the sled method. The system is compact, lightweight, portable and convenient to use. The device can be installed on the sueding machine as an on-line real-time testing system for fabric surface softness, thus improving the response efficiency of the process parameters of the sueding machine. Moreover, it can be used to construct a database of these parameters, store different fabric types, process quality requirements and reproduce processing at the same quality, in order to automatically, rapidly and accurately calibrate the parameters. This study applied the Taguchi method on the process parameters of the sueding machine to analyze the interaction between parameters, design the orthogonal array according to actual needs, and process the grinded fabric on-line. It also measured the testing system and plotted the Signal-to-Noise (S/N) ratio response table based on the S/N calculated for the parameter combinations. In the table, the larger S/N ratios represent a better quality characteristic and smaller variation. Hence, the main effect of the control factors and the optimum factor level can be obtained. After determining the optimal combination of process parameters, the process and measuring experiment of the reference group was conducted with these control factors to verify the results. It was proven that the device can obtain the maximum surface softness, and effectively and accurately test the surface softness of finished fabrics.

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