Prediction Method for Winding Parameters in Label Converting Process with Data Mining Tools

This paper describes how developed models of data mining tools can be used for the prediction of initial tension parameters and winding speed for each new design of shrink sleeve labels. The aim of this paper is to choose significant factors and build a model in the learning process using the collected data. The model can be used for prediction of key winding parameters of each new design of a shrink sleeve label. This saves time for experimental selection during the conversion of winding parameters and minimizes the risk of occurrence of defects with incorrect winding parameters.