Prediction of Hot Glue Content for Sealing Toothpaste Carton

This research compared 2 types of model (regression model and artificial neural network) for prediction of glue content for sealing toothpaste carton from 4 sealing process factors, i.e., production line, diameter of toothpaste tube, pressure in glue nozzle during applying glue onto a toothpaste carton and glue temperature in a glue tank. Models under study included 3 regression models, i.e., multiple regression, polynomial regression and stepwise regression,and backpropagation neural network (BPN). The results indicated that the BPN model possessed higher prediction accuracy and generalization capability and lower bias. The best BPN model had a structure of 4-10-1 with the mean absolute error (MAE) of validating data set of 0.04 gram. In addition, the BPN model identified that the most influential sealing process factors affecting the prediction of glue content were pressure in glue nozzle and glue temperaturein the glue tank. The packing department should concentrate on monitoring the value of both factors to control the consistency of glue usage.