The process input data which materials processing operations can collect for each unit of production is extensive. Large datasets have long been difficult to work with as computing power to execute analysis in a timely fashion was unavailable. Further, the great velocity at which the data is generated makes near real-time decision-making unwieldy without a new set of tools with which to do the work. When troubleshooting by a small dataset, such as the last few hours of production, observations made on the measured parameters can be misleading. Machine learning is opening doors to high-dimensional data analysis in material processing. In this work, high-pressure die-casting (HPDC) is explored as an exemplar of high-volume materials processing. HPDC process summary data from a full year of production data covering over 950,000 machine cycles is analyzed via supervised machine learning methods to successfully model the prediction of good parts and process scrap as determined by the die-casting machine. Additionally, the prediction of ultimate tensile strength via a classification method of extracted tensile bars is performed and the important features identified are discussed. Supervised learning is found to be a useful tool for materials processing applications.

材料加工操作可以为每个生产单元收集的过程输入数据是广泛的。长期以来，大型数据集一直难以使用，因为无法提供及时执行分析的计算能力。此外，如果没有一套新的工具来进行工作，那么生成数据的速度很快就难以进行近乎实时的决策。在通过较小的数据集（例如生产的最后几个小时）进行故障排除时，对测得参数的观察可能会产生误导。机器学习为材料加工中的高维数据分析打开了大门。在这项工作中，高压模铸（HPDC）被研究为大批量材料加工的典范。HPDC流程摘要数据来自一整年的生产数据，涵盖950多个，通过监督的机器学习方法对000个机器周期进行了分析，以成功地对压铸机确定的优质零件和加工废料的预测进行建模。此外，还通过提取的拉伸钢筋的分类方法对极限抗拉强度进行了预测，并对确定的重要特征进行了讨论。监督学习被发现是用于材料加工应用的有用工具。