Workshop Proceedings For electronic products at Bosch, internal defects are a type of anomaly that may happen and cause severe degradation in the electronic product quality. Normally, the data for analyzing internal defects in the Surface-Mount Technology (SMT) area is heterogeneous and disconnected. Diferent semantic interpretations of data are also present. This leads to various disconnected data silos (as engineered, as supplied, as produced) along the product lifecycle that cause not only huge but also repeated eforts for data clarification, collection, cleaning, and analysis. Despite the existing eforts to tackle these problems at Bosch [ 1], still a concrete solution that can be used world-wide for the semantic integration in SMT is missing. To address the above challenges, we introduce a Knowledge Graph (KG)-based approach that provides a data-driven solution to make all data transparent, semantically-enriched, and easy to access along the entire electronic product lifecycle from engineering to supply to production.
CEUR ceur-ws.org
CEUR Workshop Proceedings developing applications. Based on the KG, graph-based AI algorithms, e.g., subgraph mining, graph neural networks, are easy to be deployed for failure analysis and prediction during the design and production phases of electronic components.
Traditional approaches for identifying internal defects of electronic products are quite laborious
and time-consuming. Now thanks to KG, this process has been shortened from three months
to three minutes. This is all because the heterogeneous data sources have been semantically
enriched, integrated, and standardized so that new insights can be derived from the data to
support cost reduction tasks. By the use of our KG-based approach, we improve the eficiency of
data analysis by 70% also for efort reduction and save around 200k EUR per year across Bosch
factories. Lessons that are learned from this project can be generalized to a wider range of
KG-based projects, e.g., it is recommended to follow the best practices for developing ontologies;
KG is a suitable solution for mitigating Semantic Interoperability Conflicts (SICs) [