Guo Feng, Han Dosuck, Kim Naksoo
Department of Mechanical Engineering, Sogang University, Seoul 04107, Republic of Korea.
Polymers (Basel). 2024 Aug 7;16(16):2247. doi: 10.3390/polym16162247.
An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.
一种智能优化技术已被提出，以提高PA6-20CF碳纤维增强聚合物（CFRP）注塑成型（PIM）产品的多种结构性能。该方法集成了深度神经网络（DNN）、非支配排序遗传算法II（NSGA-II）和蒙特卡罗模拟（MCS），统称为DNN-GA-MCS策略。主要目标是确定复杂的工艺参数，同时阐明加工方法与材料性能之间的内在关系。为实现这一目标，针对汽车前发动机罩面板的PIM结构性能进行了数值研究，考虑了纤维取向张量（FOT）、翘曲和等效塑性应变（PEEQ）。将模具温度、熔体温度、保压压力、保压时间、注射时间、冷却温度和冷却时间用作设计变量。随后，通过遗传算法对成型工艺参数进行多目标优化。Z分数归一化指标的使用为评估综合目标函数提供了一个稳健的框架。PIM中的数值目标响应极其复杂，但DNN-GA-MCS策略提供的稳定性确保了精确结果的准确性。验证了全局和局部多目标对模塑聚合物-金属混合（PMH）前罩面板的增强效果，数值结果表明该策略可以快速准确地选择最佳工艺参数设置。与训练集平均值相比，各目标分别提高了8.63%、6.61%和9.75%。与全AA 5083罩面板方案相比，我们的设计减轻了16.67%的重量，在横向、纵向和扭转应变能方面分别取得了92.54%、93.75%和106.85%的成果。总之，我们提出的方法在改善……方面显示出巨大潜力，突出了其对结构性能优化的重大影响。
