基于 GENI-SD 的定制化印制电路板工序重要性评估.

Accurately estimating the critical operations that affect the quality of customized printed circuit board (PCB) can better guide enterprises in lean management of product quality. However, the common methods of enterprises are difficult to use the deep semantics between operation-related entities. To solve this problem, this paper constructed a knowledge graph of operation-related entities and proposed GENI-SD to estimate the importance of PCB operation. Firstly, this paper employed the knowledge graph to represent the relationship between operation-related entities, and utilized the graph neural network model GENI to estimate the importance of operation nodes. Secondly, this paper improved GENI and established a novel model called as GENI-SD by introducing a sampling module based on an improved predicate-aware attention mechanism and a score aggregation considering the direction of adjacent edges, thus to resolve noisy neighbors and non-direction aggregation shortcoming of GENI. Finally, it conducted experiments on the real data of PCB workshop. The results demonstrate that GENI-SD outperforms SOTA and obtains 6. 78% and 0. 71 % improvement in Spearman and NDCG@ 10 metrics comparing to that of GEN! respectively. This study provides a novel and effective method for estimating the importance of operation. [ABSTRACT FROM AUTHOR]