Abstract Melt pressure is crucial in injection molding. A variety of pressure sensors have been installed in injection molding machines and molds to collect melt-pressure information. Many methods have been developed to extract the features of melt pressure. However, challenges exist for these feature extraction methods because they are, inevitably, extremely dependent on manual operation. In this study, an unsupervised feature extraction method using a sparse autoencoder is proposed to extract the features of melt pressure during injection molding. An injection molding model was applied to reinterpret the network structure of a sparse autoencoder to better understand the network structure. The feature curve, which is defined as the curve of weights between input and hidden units, was proved to be a significant indicator to measure how melt compression causes variations in injection pressure. Over 10,000 shots were conducted to verify the proposed feature extraction method. The experimental and simulation results show that the feature curve effectively extracts switching points (gate and velocity-pressure switchover), important indicators (viscosity index, holding pressure, and peak pressure) in the injection molding process and shows how the geometric features of a part affect the melt flow.

中文翻译：

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注塑成型过程中熔体压力的特征提取和物理解释

摘要 熔体压力在注塑成型中至关重要。各种压力传感器已安装在注塑机和模具中，以收集熔体压力信息。已经开发了许多方法来提取熔体压力的特征。然而，这些特征提取方法存在挑战，因为它们不可避免地极其依赖于手动操作。在这项研究中，提出了一种使用稀疏自编码器的无监督特征提取方法来提取注塑过程中的熔体压力特征。应用注塑模型重新解释稀疏自编码器的网络结构，以更好地理解网络结构。特征曲线，定义为输入和隐藏单元之间的权重曲线，被证明是衡量熔体压缩如何导致注射压力变化的重要指标。进行了超过 10,000 次拍摄以验证所提出的特征提取方法。实验和仿真结果表明，特征曲线有效地提取了注塑过程中的切换点（浇口和速度-压力切换）、重要指标（粘度指数、保压压力和峰值压力），并显示了零件的几何特征如何影响熔体流动。