AI-Driven Data Consistency and Relationship Inference System for Agile Component Library Management

Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has shown an ability to create parts ~30 times faster than manual methods with 98.81% accuracy. The novelty of this system is two-fold. First, it represents a unique integration of AI-based attribute inference and relationship reasoning for improving component library data quality. Second, the system enables a new paradigm of on-demand component creation within Amoeba software that allows engineering teams to obtain tailored components immediately rather than waiting for delivery from librarians. By enabling agile component library management and maintaining data integrity, the system brings benefits in the environment of Industry 4.0 and the increasing digitization of engineering processes.