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This article 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 3-D convolutional neural networks integrated with computer-aided engineering simulations for RC isolation. Compared with the existing methods, the proposed approach: 1) addresses a novel problem of applying deep learning for object shape error identification instead of object detection; 2) overcomes fundamental performance limitations of current linear approaches for RC analysis (RCA) of assembly systems that cannot be used on point cloud data; and 3) provides capabilities for unsolved challenges such as ill-conditioning, fault-multiplicity, RC prediction with uncertainty quantification, and learning at design phase when no measurement data are available. Comprehensive benchmarking with existing machine learning models demonstrates superior performance with R<sup>2</sup> = 0.98 and MAE = 0.05 mm, thus improving RCA capabilities by 29%.

Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts