Using Intelligent Control Systems to Predict Textile Yarn Quality

作者：Nurwaha, Deogratias^*; Wang, Xin Hou

来源：Fibres & Textiles in Eastern Europe, 2012, 20(1): 23-27.

• intelligent techniques
• CSP
• fibre properties

This study describes the application of intelligent control systems in textile engineering and how to use these approaches for developing a spun yarn quality prediction system. The Multilayer Perceptron Neural Network(MLPNN), Support Vector Machines(SVMs), the Radial Basic Function Network(RBFN), the General Neural Network(GNN), the Group Method of Data Handling Polynomial Neural Network (GMDHPNN) and Gene expression Programming (GEP), generally called intelligent techniques, were used to predict the count-strength-product (CSP). Fiber properties such fibre strength (FS), micronaire (M), the upper half mean length (UHML), fibre elongation(FE), the uniformity index (UI), yellowness (Y), grayness (G) and short fibre content (SFC) were used as inputs. The prediction performances are compared to those provided by the classical Linear Regression (LR) model. The SVMs model provides good prediction ability, followed by the GEP and LR models, respectively. Graphs illustrating the relative importance of fibre properties for CSP were plotted. Fiber strength (FS) is ranked first in importance as a contributor to CSP by the five models, while fibre elongation (FE) ranks second. By means of the yarn strength learned surfaces on fibre properties, the study shows how to control yarn quality using knowledge of fibre properties.

• 出版日期2012-2
• 单位东华大学