Numerical simulations of polymer melt flow behavior in cavities help predict and optimize injection molding process parameters. However, simulation and actual results may differ because of simplified mathematical models, inaccurate processing conditions, material property settings, and machine aging, among other factors. Therefore, simulated optimal process parameters cannot be directly applied in practice. This study applied machine learning to generate a virtual–actual correction model to improve the accuracy of simulation results, especially the cavity pressure profile, a key indicator of injection-molding quality for identifying ideal process parameter settings such as filling-to-packing switchover time and holding pressure. This method does not require big data for model training to enhance its practicality. Therefore, the correction model is only suitable for specific settings. A set of injection molding machines, molds, and processed materials were used for experimental verification. An autoencoder model was used to extract the features of simulation and actual cavity pressure curves. Then, a multilayer perceptron model was used to determine a relationship between simulation and actual features. The autoencoder was used to decode simulated features into cavity pressure curves. The proposed method was verified with dumbbell-shaped specimens; the correlation between simulated and actual cavity pressures was greatly improved from 81% to 98%.

中文翻译：

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使用自动编码器和多层感知器神经网络校准腔压力模拟

模腔中聚合物熔体流动行为的数值模拟有助于预测和优化注塑工艺参数。但是，由于简化的数学模型、不准确的加工条件、材料属性设置和机器老化等因素，模拟结果和实际结果可能会有所不同。因此，模拟的最优工艺参数不能直接应用于实际。本研究应用机器学习生成虚拟 - 实际校正模型，以提高模拟结果的准确性，尤其是型腔压力曲线，这是注塑成型质量的关键指标，用于确定理想的工艺参数设置，例如填充到保压的转换时间和保持压力。这种方法不需要大数据进行模型训练，以增强其实用性。所以，校正模型仅适用于特定设置。使用一套注塑机、模具和加工材料进行实验验证。使用自动编码器模型来提取模拟和实际腔压力曲线的特征。然后，使用多层感知器模型来确定模拟和实际特征之间的关系。自编码器用于将模拟特征解码为腔压力曲线。所提出的方法通过哑铃形试样进行了验证；模拟和实际模腔压力之间的相关性从 81% 大大提高到 98%。使用自动编码器模型来提取模拟和实际腔压力曲线的特征。然后，使用多层感知器模型来确定模拟和实际特征之间的关系。自编码器用于将模拟特征解码为腔压力曲线。所提出的方法通过哑铃形试样进行了验证；模拟和实际模腔压力之间的相关性从 81% 大大提高到 98%。使用自动编码器模型来提取模拟和实际腔压力曲线的特征。然后，使用多层感知器模型来确定模拟和实际特征之间的关系。自编码器用于将模拟特征解码为腔压力曲线。所提出的方法通过哑铃形试样进行了验证；模拟和实际模腔压力之间的相关性从 81% 大大提高到 98%。