智能注射成形中工艺参数的多目标自学习优化

注射成形工艺参数是保障产品质量的关键因素。传统试错法严重依赖工艺人员的试模经验,随着注射成形工艺广泛应用于电子、航空航天等国家战略领域,产品的高端化对工艺参数智能化设置水平提出更高的要求。由于成形产品存在多方面的质量要求,且不同质量指标间可能相互制约,因此亟需一种工艺参数多目标智能优化方法,以获得不同优化目标间的帕累托最优。已有学者利用智能优化方法,如非支配排序遗传算法等,对多目标优化问题进行求解,但是此类方法需大量样本数据对质量-参数关系进行建模,存在试验次数多、且对不同材料及模具的适应性较差等问题。为解决上述问题,提出一种注射成形工艺参数多目标自学习优化方法,在优化过程中实时计算并更新各个工艺参数的梯度,并由不同质量指标的多梯度下降算法对多个目标函数进行优化,在优化过程中实现各工艺参数对产品质量影响程度的自主学习,省去了采集大量数据来建立多个质量模型的过程,实现了注射成形工艺参数的高效智能优化。在基准测试函数实验中,所提方法的优化结果与理论解的相对误差小于2%。同时数值仿真与注射成形实验结果表明,所提方法能高效获得多个优化目标的帕累托最优。

Multi-objective self-learning optimization method for processparameters 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.