Materials processing is a critical subset of manufacturing which is benefitting by implementing machine learning to create knowledge from the data mined/collected and gain a deeper understanding of manufacturing processes. In this study, we focus on aluminum high-pressure die-casting (HPDC) process, which constitutes over 60% of all cast Al components. Routinely collected process data over a year’s time of serial production are used to make predictions on mechanical properties of castings, specifically, the ultimate tensile strength (UTS). Random Forest, Support Vector Machine (SVM), and XGBoost regression algorithms were selected from the machine learning spectrum along with a Neural Network, a deep learning method. These methods were evaluated and assessed and were compared to predictions based on historical data. Machine learning, including Neural Network, regression models do improve the predictability of UTS above that of predicting the mean from prior tests. Choosing the correct models to use for the data requires an understanding of the bias-variance trade-off such that a balance is struck between the complexity of the algorithms chosen and the size of the dataset in question. These concepts are reviewed and discussed in context of HPDC.

物料处理是制造业的关键子集，它通过实施机器学习从挖掘/收集的数据中创建知识并获得对制造过程的更深入了解而受益。在这项研究中，我们专注于铝高压压铸（HPDC）工艺，该工艺占所有铸造Al组件的60％以上。在连续生产一年的时间内定期收集的工艺数据用于预测铸件的机械性能，特别是极限抗拉强度（UTS）。从机器学习范围中选择了随机森林，支持向量机（SVM）和XGBoost回归算法，以及深度学习方法神经网络。对这些方法进行了评估和评估，并与基于历史数据的预测进行了比较。机器学习 包括神经网络在内，回归模型的确提高了UTS的可预测性，超过了先前测试预测均值的可预测性。选择用于数据的正确模型需要了解偏差方差的折衷，以便在所选算法的复杂性和相关数据集的大小之间取得平衡。这些概念是在HPDC的背景下进行审查和讨论的。