Descriptive and predictive statistical modeling of a ring spinning process

Abstract

In the textile industry, spinning process is one of the most important stages to determine the yarn structure, on which the desired fabric properties highly depend, throughout the manufacturing chain. In the production of wool yarn, \ring spinning" is the most frequently used technology since wool is principally ring-spun. In the current study, it is aimed to determine how probability of end breakage, which is one of the most important quality variables, changes with respect to process variables using statistical methods. Process data are collected under normal operating conditions of YUNSA Worsted and Woolen Company in Turkey between 2012-2014. Nominal process variables consist of color and composition of the fed ber, ring spinning machine number, spinning, and twist direction, while continuous process variables are lot size, roving count, draft, twist level, ring traveler number, yarn count, spindle speed, and machine age. In the rst part of the study, Principal Component Analysis (PCA) is used to examine how historical operating conditions described by continuous process variables and binary nominal variables change for di erent runs, while Correspondence Analysis (CA) is employed to elucidate which machines are preferred for certain operating conditions. In the second part, failure probability is modeled with respect to process variables using logistic regression. The predictive powers of the regression models constructed for the rst, second and third types of machines, area under its ROC was found to be 0.66, 0.70 and 0.75 with optimal true positive and false positive rates as 0.64 and 0.40, 0.67 and 0.40, and 0.65 and 0.28, respectively.