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

Abstract

The end breakage rate (EBR), which is one of the most important quality variables used to determine the yield of a spinning process, depends on various process conditions and fiber/yarn properties. In the current study, historical data consisting of more than 10,000 runs from 55 ring spinning machines recorded under normal operation in YUNSA Worsted and Woolen Company in Turkey were analyzed using exploratory and predictive statistical techniques. Principal Component Analysis (PCA) was used to determine subsets of quantitative variables, which vary collectively, forming clusters for different machine types. Correspondence Analysis (CA) was found to be particularly beneficial to determine the association between machines and nominal variables, which make a significant contribution to product quality in textile industries. The current spinning process requires accurate discrimination between acceptable and faulty yarns, determined via a threshold on the EBR, so logistic regression was utilized for the prediction of faulty yarns. The Receiver Operating Characteristic curves showed that the discriminative capacity of the logistic models was at an acceptable level, almost on a par with that of Artificial Neural Network (ANN) models. For different types of machines, while yarn count, roving count, lot size, twist level and composition were commonly present in logistic models, the magnitude of their partial effects varied significantly. In conclusion, PCA, CA and logistic regression are suggested, along with ANN models, to be used for textile industries in online monitoring, detecting faulty machines, choosing optimum machines for specific operational conditions and determining the range of process variables for which controlled experiments may be required.