Research on Optimization Method of Injection Molding Machine Temperature Control System Based on IK-SS

Injection molding, as a plastic processing technology, is widely used in the mass production of plastic products with high precision and automation. However, the performance optimization process of this system currently faces some prominent issues, such as time consumption, reliance on experience, and difficulty in achieving optimality. Traditional optimization methods typically rely on manual experience and rules, which are inefficient and time-consuming. To enhance the performance of the control system, this paper focuses on utilizing an improved simplex search method based on the data-driven optimization concept to optimize the parameters of the temperature control system of the injection molding machine. The method introduced in this paper is termed the Knowledge-Informed Simplex Search Method Based on Adjacent Historical Centroid Points Information (IK-SS). The IK-SS algorithm is a variant of the simplex search method that utilizes information from the centroids of neighboring simplices to evaluate the current state of the optimization process and dynamically adjust the iteration step size. This approach aims to improve optimization efficiency while reducing computational complexity and time costs. Simulation and experimental validation demonstrate that the IK-SS algorithm significantly enhances the temperature control performance of the injection molding machine (IMM). This demonstrates the potential of the IK-SS algorithm as a tool for optimizing the temperature control system of injection molding machines.

I. Introduction

The temperature control system of an injection molding machine(IMM) is of vital importance in ensuring the quality and efficiency of the injection molding process. It regulates the temperature within the mold, directly influencing the final product quality and production yield [1]–[2]. Any deviations from the desired temperature can lead to defects in molded parts and decreased productivity. Improper temperature control is a significant contributor to defects in injection molded products. Thus, optimizing the temperature control system is essential for improving quality and efficiency. This optimization involves determining the best settings for controller parameters, similar to quality control in batch processes. Data-driven optimization methods have been widely applied in various industrial processes, driving intelligent and sustainable production methodologies. However, for batch process like the temperature control system of an injection molding machine, finding optimal settings is often a laborious and time-consuming task due to the intricate correlation between configuration parameters and production outcomes.