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

Abstract

Abstract. The design of datum systems for body-in-white (BIW) assembly is critical but reliant on expert experience, leading to inefficiency and inconsistency. This article 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 geometric dimensioning and tolerancing (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 multicriteria 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.