A Knowledge-Driven Framework for Automated Datum System Design in Body-in-White Assembly

The design of datum systems for Body-in-White (BIW) assembly is critical but reliant on expert experience, leading to inefficiency and inconsistency. This paper presents a knowledge-driven framework to automate this process. The core is a Hierarchical Topology Mapping Model (HTMM) that formally represents the spatial and functional relationships among part features within the GD&T context. This model is implemented via a four-layer architecture centered on Key Reference Points (KRPs), encompassing Component, Feature, Point Cloud, and Datum levels, integrating manufacturing and structural constraints into a computable graph. A reasoning mechanism using KRP-based analysis and multi-criteria evaluation is developed to generalize datum logic across components. Validated in an industrial case study, the framework generated a datum scheme for a body side outer panel that achieved a 4.5% higher composite quality score than an expert baseline. In a generalization test across three different components (A-pillar, B-pillar, Sill), it maintained 100% compliance with all core engineering constraints. This work contributes a structured, model-based method that enhances automation, consistency, and reuse in datum design for complex assemblies.