Identification of Dimensional Variation Patterns on Compliant Assemblies

This paper presents a methodology to diagnose sources of dimensional variation for compliant parts from the measurement data of final assemblies. The method is developed for a single-station assembly process. The proposed diagnosis tool is based on applying a predictive variation propagation model to determine the part-to-part and tooling interaction in the assembly system. The variation propagation model allows identifying the impact of different faulty component patterns in the final assembly product. Using the predictive assembly fault patterns and the designated component analysis, the contribution of each fault in the total system variation may be identified. The methodology incorporates an optimal sensor placement algorithm to determine the key measurement points in the assembly. Two case studies were conducted to illustrate that the methodology is capable of identifying part variation patterns from assembly measurement data, even under significant levels of noise. Although the methodology is presented for a single assembly station, it can be extended to a multiple-station assembly scenario using a multistation variation propagation model.