Two-way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

Abstract

This paper explains the feasibility of two-way prediction by developing direct models relating fiber to yarn and reverse models relating yarn to fiber using multivariate methods simultaneously. These models evaluate the dependencies of cotton yarn properties on fiber properties and vice versa with minimum random errors and maximum accuracy. To this end, cotton fiber properties were measured from rovings carefully untwisted. An HVI system and an evenness tester of premier were used to measure the various properties. The samples of cotton yarns (108 samples) produced yarn counts ranging from 16 to 32 Ne with optimum twist factor. In this study, effective variables were selected by multivariate statistical test ( m -test). Then, multivariate analysis of variance (MANOVA) was used for evaluating the significance of obtained models. Next, the optimal separate equations were determined through multivariate multiple regression. After solving the linear equation system, a reverse model was achieved. By selecting fiber properties and machine factors as appropriate variables, the relative importance of these factors was also investigated. The results showed that the obtained equations were significant at the significance level α = 0.01.Keywords: multivariate test ( m -test); multivariate analysis of variance (MANOVA); multivariate multiple regression; cotton spinning; quality properties of yarn; cotton fiber properties IntroductionPredicting the qualitative characteristics of yarns, such as tensile properties, unevenness, and hairiness, from the raw material properties (fiber properties) has been the main purpose of many studies in recent decades. Two main approaches used in these studies are statistical and mathematical approaches. Statistical models have relatively higher predictive power than mathematical models. One of the most common statistical approaches is the multiple regression method. So far, the statistical models for the prediction of cotton yarn properties from fiber properties have been established by

Useful life prediction of woolen hand-knotted carpets using multivariate multiple regression

“…Therefore, the system of equations AX = Y has a unique solution as X = A −1 Y (Fattahi et al, 2011;Searl, 2004). Consequently, the equations of the reverse models for wear factor (x 1 ) and production factors (x 2 , x 3 , and x 4 ) were obtained as follows:…”

“…When a correlation structure among the response variables is present, a single multivariate regression is more efficient than regressions analyses for each response variable separately. If we have p-dependent variables, q parameters Therefore, we could fit p (p > 1), separate models, when there are several criterion variables as (Fattahi, Hoseini Ravandi, & Taheri, 2011;Seber, 2004):…”

“…The statistics test the multivariate hypothesis in different procedures and their p-value can be approximated by F distributions (Fattahi et al, 2011;Seber, 2004).…”

Exploratory and predictive logistic modeling of a ring spinning process using historical data

“…7,8 While general principals on the relationship between spinning conditions and yarn quality have been established, determining universal models with high predictive power has been elusive, mainly due to nonlinear relations between spinning process variables and the end breakage mechanism, 9,10 and variations in raw materials and machines. 11,12 In the literature, predictive models between yarn properties and spinning process conditions have been constructed using controlled experiments or historical data from specific processes, mainly via linear regression [13][14][15][16][17][18] and Artificial Neural Network (ANN) [14][15][16]18,19 methods. While point predictions from ANN models have been generally found to be superior to those from linear regression models, lack of an explicit representation of the estimated function, and lack of statistics, such as P-values and confidence intervals (CIs), in ANN models make it difficult to interpret the resulting model, assign significance to the variables and measure the precision of the predictions.…”