Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
This addresses an incremental problem for the manufacturing industry by potentially reducing costs and waste in car production through data-driven solutions.
The paper investigated applying knowledge graph embedding (KGE) to quality monitoring for welding in manufacturing, specifically predicting welding spot diameter and car body assignment, and found both limitations and promising aspects in adapted KGE methods on real industry data.
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.