Multi-objective self-learning optimization method for process parameters in intelligent injection molding

The process parameters of injection molding are key factors to ensure product quality. The traditional trial-and-error method relies heavily on the personal experience. The injection molding process is widely used in many important fields, such as electronics, aerospace, etc. The high-end products put forward higher requirements for the intelligent setting of process parameters. Since there are various quality requirements for molded products, and different quality indicators may restrict each other, an intelligent multi-objective optimization method of process parameters is urgently needed to obtain the Pareto optimum among different optimization objectives. Scholars have proposed some intelligent optimization methods. For example, non-dominated sorting genetic algorithms are used to solve multi-objective optimization problems. However, a big amount of sample data are required in such methods to model the quality-parameter relationship. There are problems of a large number of experiments and the poor adaptability of the different materials and molds. To address these issues, proposes a multi-objective self-learning optimization method for injection molding process parameters for the first time. During the optimization process, the gradient of each process parameter is calculated and updated in real time. The multi-gradient descent algorithm is conducted to optimize different quality indicators. In the optimization process, the self-learning of the influence of each process parameter is realized, which removes the need to perform large numbers of experiments for optimization model establishment. In this way, the efficient intelligent optimization of injection molding process parameters is realized. The relative error between the optimization result of this method and the analytical solution in the benchmark test function is smaller than 2%. Numerical simulation and experimental results show that this method can obtain the Pareto optimum of multiple optimization objectives efficiently. © 2021, Science Press. All right reserved.