Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is; and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.

翻译：近年来，知识图谱嵌入（KGE）领域涌现了一系列研究，旨在通过机器学习（ML）将实体和关系的嵌入表示为数值向量及数学映射。然而，针对制造业工业问题应用KGE的研究仍十分有限。本文探讨了KGE能否以及能在多大程度上解决制造业中的一个重要问题——焊接质量监测。焊接是每年数百万辆汽车生产过程中极具影响力的关键工序。本研究与博世公司寻求数据驱动解决方案的研究方向一致，旨在替代传统的破坏性检测方法（该方法成本极高且会产生废弃物）。本文同时解决了两个极具挑战性的问题：焊接点直径的大小判定，以及该焊点所属的车身型号。由于需将大量车身作为类别标签进行分配，该问题对传统ML方法构成显著困难。我们将该问题建模为链接预测任务，在考虑字面量信息的条件下，基于真实工业数据对主流KGE方法进行了实验。实验结果揭示了改进型KGE方法的局限性及其应用潜力。