Object Shape Error Response using Bayesian 3D Convolutional Neural Networks for Assembly Systems with Compliant Parts

The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to assembly process parameters, which can be interpreted as root causes (RC) of the object shape defects caused by the assembly system. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks for RC isolation. Compared with the existing methods, the proposed approach (i) addresses a novel problem of applying deep learning for object shape error identification instead of nominal shape identification (object detection); and, (ii) overcome significant limitations in current approaches for Root Cause Analysis (RCA) of assembly systems which are frequently ill-conditioned (collinear fault patterns) with high fault-multiplicity (frequently encountered in 6-sigma strategy) while quantifying the prediction uncertainty. The verification of the proposed methodology is conducted based on the CAE model of a multi-stage assembly process for automotive SUV door made of compliant parts. The results demonstrate performance with R-Squared (\pmbR
²
)=0.98 and Mean Absolute Error (MAE)=0.05 mm, thus improving RCA capabilities by 29% as compared to state-of-the-art methods.