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

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.

[1]  Soroosh Sorooshian,et al.  Correspondence analysis with MATLAB , 1993 .

[2]  Saeid Fattahi,et al.  Two‐way prediction of cotton yarn properties and fiber properties using multivariate multiple regression , 2011 .

[3]  George R. Franke,et al.  Correspondence Analysis: Graphical Representation of Categorical Data in Marketing Research , 1986 .

[4]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[5]  C. A. Lawrence,et al.  Fundamentals of Spun Yarn Technology , 2003 .

[6]  Samar Ahmed Mohsen Abd-Ellatif,et al.  Optimizing sliver quality using Artificial Neural Networks in ring spinning , 2013 .

[7]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[8]  Yukio Hori.  A Statistical Analysis of Yarn Breakage on Spinning Frame , 1955 .

[9]  A. Akobeng,et al.  Understanding diagnostic tests 3: receiver operating characteristic curves , 2007, Acta paediatrica.

[10]  B. Purushothama.  A Practical Guide to Quality Management in Spinning , 2011 .

[11]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[12]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[13]  Michael T. Bendixen,et al.  A Practical Guide to the Use of Correspondence Analysis in Marketing Research , 1996 .

[14]  Rocco Furferi,et al.  Yarn Strength Prediction: A Practical Model Based on Artificial Neural Networks , 2010 .

[15]  D. R. Cox,et al.  Interpretation of interaction: A review , 2007, 0712.1106.

[16]  Mustafa E. Üreyen,et al.  Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties , 2008 .

[17]  Yildiray Turhan,et al.  Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network , 2013 .

[18]  W. S. Simpson,et al.  Wool : Science and technology , 2002 .

[19]  D. Hosmer,et al.  A comparison of goodness-of-fit tests for the logistic regression model. , 1997, Statistics in medicine.

[20]  W. Oxenham,et al.  Predicting End Breakage Rates in Worsted Spinning , 1994 .

[21]  A. Akobeng.  Understanding diagnostic tests 1: sensitivity, specificity and predictive values , 2007, Acta paediatrica.

[22]  M. El-Sayed.  OPTIMIZING RING SPINNING VARIABLES AND A PROPOSED PROCEDURE TO DETERMINE THE EGYPTIAN COTTON SPINNING POTENTIAL , 2007, Egyptian Journal of Agricultural Research.

[23]  Zheng-Xue Tang,et al.  Simulation and experimental validation of a ring spinning process , 2006, Simul. Model. Pract. Theory.

[24]  William H. Woodall,et al.  Control Charts Based on Attribute Data: Bibliography and Review , 1997 .

[25]  Henk A. L. Kiers,et al.  CAR: A MATLAB Package to Compute Correspondence Analysis with Rotations , 2009 .

[26]  C. Braak.  Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis , 1986 .

[27]  Anindya Ghosh,et al.  THE MECHANISM OF END BREAKAGE IN RING SPINNING: A STATISTICAL MODEL TO PREDICT THE END BREAK IN RING SPINNING , 2004 .

[28]  Barry M. Wise,et al.  Application of multi-way principal components analysis to nuclear waste storage tank monitoring , 1996 .

[29]  C. A. Lawrence,et al.  Advances in yarn spinning technology , 2010 .

[30]  Hervé Abdi,et al.  Correspondence Analysis , 2014, Encyclopedia of Social Network Analysis and Mining.

[31]  Lijing Wang,et al.  Mill specific prediction of worsted yarn performance , 2006 .

[32]  Z. Stjepanovic,et al.  Compact Spinning for Improved Quality of Ring - Spun Yarns , 2003 .

[33]  Galit Shmueli,et al.  To Explain or To Predict? , 2010, 1101.0891.

[34]  A. A. Almetwally,et al.  Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models , 2014 .

[35]  Stanley Lemeshow,et al.  Applied Logistic Regression, Second Edition , 1989 .

[36]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[37]  Mustafa E. Üreyen,et al.  Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. II. Prediction of yarn hairiness and unevenness , 2008 .

[38]  Xungai Wang,et al.  Predicting Worsted Spinning Performance with an Artificial Neural Network Model , 2004 .

[39]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[40]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[41]  Zhao Bo.  Prediction of end breakage rates of cotton yarn in ring spinning processing by applying neural network approach and regression analysis theory , 2012, 2012 International Conference on System Science and Engineering (ICSSE).

[42]  A. P. Schaffers,et al.  CO-CORRESPONDENCE ANALYSIS: A NEW ORDINATION METHOD TO RELATE TWO COMMUNITY COMPOSITIONS , 2004 .

[43]  Edward L. Korn,et al.  Simultaneous Testing of Regression Coefficients with Complex Survey Data: Use of Bonferroni t Statistics , 1990 .

[44]  P. Majumdar,et al.  Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models , 2004 .

[45]  A. A. Almetwally,et al.  Effects of spandex drawing ratio and weave structure on the physical properties of cotton/spandex woven fabrics , 2014 .

[46]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[47]  Ali Cinar,et al.  Diagnosis of process disturbances by statistical distance and angle measures , 1997 .

[48]  W. Oxenham,et al.  Predicting End Breakage Rates in Worsted Spinning , 1994 .

[49]  David Afshartous,et al.  Key Results of Interaction Models with Centering , 2011 .

[50]  M. Prendzova.  The Effect of Cotton Yarn Properties on Yarn End Breakage , 2000 .

[51]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[52]  Anindya Ghosh,et al.  PREDICTIVE MODELS FOR STRENGTH OF SPUN YARNS: AN OVERVIEW , 2005 .

[53]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .