Artificial intelligence (AI) is integral to Industry 4.0 and the evolution of smart factories. To realize this future, material processing industries are embarking on adopting AI technologies into their enterprise and plants; however, like all new technologies, there is always the potential for misuse or the false belief that the outcomes are reliable. The goal of this paper is to provide context for the application of machine learning to materials processing. The general landscapes of data science and materials processing are presented, using the foundry and the metal casting industry as an exemplar. The challenges that exist with typical foundry data are that the data are unbalanced, semi-supervised, heterogeneous, and limited in sample size. Data science methods to address these issues are presented and discussed. The elements of a data science project are outlined and illustrated by a case study using sand cast foundry data. Finally, a prospective view of the application of data science to materials processing and the impact this will have in the field are given.

人工智能（AI）是工业4.0和智能工厂发展不可或缺的一部分。为了实现这一未来，材料加工行业正在着手在其企业和工厂中采用AI技术。但是，像所有新技术一样，总有可能会滥用或误认为结果是可靠的。本文的目的是为机器学习在材料处理中的应用提供背景。以铸造厂和金属铸造行业为例，介绍了数据科学和材料处理的一般情况。典型的铸造数据所面临的挑战是数据不平衡，半监督，异构且样本数量有限。提出并讨论了解决这些问题的数据科学方法。通过使用砂铸铸造数据的案例研究，概述和说明了数据科学项目的要素。最后，给出了对数据科学在材料处理中的应用的前瞻性看法，以及这将对该领域产生的影响。