论文标题
注射成型制造中自动质量控制的机器学习
Machine learning for automated quality control in injection moulding manufacturing
论文作者
论文摘要
机器学习（ML）可以改善和自动化质量控制（QC）在注塑制造中。但是，由于广泛的，现实世界的流程数据的标签成本很高，但是，模拟过程数据的使用可能会为成功实施提供第一步。在这项研究中，模拟数据用于开发一个预测模型，以针对注射成型排序容器的产品质量。测试集的精度，特异性和敏感性分别为$ 99.4 \％$，$ 99.7 \％$和$ 94.7 \％$ $。因此，这项研究表明了ML在注射成型中对自动化质量控制的潜力，并鼓励向接受现实世界数据训练的ML模型扩展。
Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was $99.4\%$, $99.7\%$ and $94.7\%$, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.
