Optimization of Process Parameters Based on Machine Learning and Determination of Relative Importance of Process Variables on Jute Ply Yarn Breaking Extension

Abstract

The breaking extension of jute ply yarn is comparatively low and ordinarily varies from 1.5 – 3.5% depending upon the process parameters like number of plies, single yarn twist factor and ply to single yarn twist ratio. To achieve various levels of jute ply yarn breaking extension within the above range, optimization of the three process variables was done in this study using a machine learning-based decision tree. For such optimization, twenty-seven different types of jute ply yarns were produced using three levels of singles' twist factor, number of ply and ply-to-single twist ratio. A total of 216 observed breaking extension values of these yarns were used for regression-based machine learning wherein 67% of test data were used to train the mode and the remaining 33% of test data were used for validation. An 8-node decision tree thus achieved from the model was used for the optimization process. A boxplot vs. terminal node graph was also used for classified optimization of ply breaking extension for various levels. The study conducted in this work reveals that the breaking extension of jute ply yarn varies directly with the number of plies, where 4-ply jute yarn produces maximum breaking extension and 2-ply produces the minimum. It was also observed that the decision tree was useful for the judicial selection of process parameters to achieve various levels of jute ply yarn breaking extension, wherein, it was found that the critical values for ply to single yarn twist ratio and single yarn twist factor were 0.80 and 26. The study also shows that apart from the individual influence of the variables on the breaking extension of ply yarns, interactions between variables also influence the breaking extension of jute ply yarn.

Keywords

• optimization
• machine learning
• jute ply yarn
• breaking extension