Duplicate components in large-scale Digital Mock-Up (DMU) assemblies present a significant challenge to product integrity within Digital Twin environments operating on integrated OEM Product Lifecycle Management (PLM) platforms. These duplicates, often referred to as structural redundancies or doubloons, are characterized by geometrically identical or equivalent components occupying the same spatial location without corresponding justification in the Bill of Materials (BOM). Such inconsistencies arise from increasing assembly complexity, tool integration issues, and limitations of conventional scripting-based validation approaches.
Existing duplicate detection methods rely heavily on manual inspection and isolated tool functionalities, making them impractical for large-scale assemblies. While interference analysis tools can detect spatial conflicts, they cannot reliably distinguish between valid engineering overlaps and unintended duplicate conditions. Consequently, a more structured and automated validation strategy is required.
This paper presents an automated computer vision-based methodology developed within an OEM Digital Twin environment to address these challenges. The proposed framework combines interference-data extraction, image-based overlap assessment, automated duplicate screening, Active Feedback Learning, and BOM-integrated validation within a unified workflow. The methodology transforms interference-derived information into structured analytical features and uses Red Percentage–based overlap evaluation to identify potential duplicate conditions.
The proposed approach enables scalable duplicate detection across complex assemblies while reducing dependence on manual review. Validation on production-scale automotive DMU datasets demonstrated significant reductions in engineering effort and substantial filtering of non-actionable interference records. By integrating computer vision analytics, feedback-driven noise elimination, and product structure validation, the proposed framework improves Digital Twin data integrity and supports efficient engineering workflows in modern OEM PLM environments.