Abstract:

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.