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 process parameters, which can be interpreted as root causes (RC) of the object shape defects. The OSER approach leverages Bayesian 3D-Convolutional Neural Networks integrated with Computer-Aided Engineering (CAE) simulations for RC isolation. Compared with existing methods, the proposed approach (i)addresses a novel problem of applying deep learning for object shape error identification instead of object detection; (ii)overcomes fundamental performance limitations of current linear approaches for Root Cause Analysis (RCA) that cannot be used on point cloud data; and, (iii)provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC isolation with uncertainty quantification and learning at design phase when no measurement data is available. Comprehensive benchmarking with machine learning models demonstrates superior performance with R2=0.98 and MAE=0.05 mm, thus improving RCA capabilities by 29%