Work

Optimization Method of Heat-Sealing Process for Pillow Packaging Machine

Year: 2025

Type: article

Abstract: Aiming at the problems of low production efficiency and high manual dependence in the heat-sealing process of the pillow packaging machine, the existing optimization methods of process parameters were improved, and an intelligent decision-making model of the longitudinal sealing heat-sealing process based on the radial basis function neural network and orthogonal least square method was proposed to realize the efficient and accurate optimization of heat-sealing process parameters. By analyzing the fracture yield strength of the composite, the target heat-sealing strength range was determined. The heat-sealing temperature, heat-sealing speed, and heat-sealing plate distance were selected as key process variables, and the actual production data were used to train the model to accurately construct the nonlinear mapping relationship between heat-sealing process parameters and heat-sealing strength. On this basis, the genetic algorithm optimization framework with the model predictive output as the fitness function is designed to realize the rapid search of the optimal combination of process parameters. The optimization results were introduced into the pillow packaging machine for a verification test. The measured heat-sealing strength was stable within the target range. The maximum error of the optimization group was less than 10%, and the average error was less than 5%, which was significantly better than the effect of manual experience. The experimental results show that the proposed method can effectively improve the efficiency and consistency of process optimization under the premise of ensuring the quality of heat-sealing, meet the requirements of automatic production for high precision and low consumption of the heat-sealing process optimization, and realize the comprehensive improvement of efficiency, accuracy, and intelligent level in the longitudinal heat-sealing process of the pillow packaging machine.

Source: Processes

DOI: 10.3390/pr13113602