Department of Polymer Engineering, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
MTA-BME Lendület Lightweight Polymer Composites Research Group, Műegyetem rkp. 3., H-1111 Budapest, Hungary.
Sensors (Basel). 2022 Apr 1;22(7):2704. doi: 10.3390/s22072704.
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8-10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.
注塑成型的基本要求之一是确保所生产零件的质量稳定。然而，大量的加工条件通常以相当复杂的方式相互关联，这使得这一目标具有挑战性。机器学习 (ML) 算法可以成为解决方案，因为它们通过学习数据集的结构在多维空间中工作。在这项研究中，我们使用了四种 ML 算法（kNN、朴素贝叶斯、线性判别分析和决策树），并比较了它们在预测多型腔注塑质量方面的有效性。我们将基于压力的质量指标（特征）用作分类算法的输入。我们证明，即使在训练数据非常少的情况下，所有经过检查的 ML 算法都能充分预测注塑成型的质量。我们发现决策树算法是最准确的，计算时间仅为 8-10 秒。决策树算法的平均性能甚至超过 90%，即使训练数据非常少。我们还表明，特征选择不会显著影响决策树算法的准确性。
