Data-Driven Framework for Dimensional Quality Control in Automotive Assembly: Integration of PCA-BP Neural Network with Traceable Deviation Source Identification

The intelligent transformation in the manufacturing industry poses challenges to traditional quality control methods, particularly in handling redundant data and ensuring model interpretability within high-dimensional, multivariate assembly processes. This study presents an integrated approach combining Principal Component Analysis (PCA), Back Propagation neural network (BP neural network), and permutation importance to improve quality prediction and traceability in the automotive body-in-white rear panel dimensional chain. The data for this study originates from the actual production process of an automotive manufacturer. It comprises direct geometric measurements from the rear panel of a specific vehicle model’s Body-in-White (BIW). The measurement points from key coordinates that influence rear panel matching serve as the numerical input variables. The corresponding measurement result from the Skeleton Assembly is utilised as the output variable, which represents the final assembly quality and is treated as a numerical variable in this model. PCA is first applied to reduce dimensionality and eliminate data redundancy. Then, two types of neural networks—single and sequential—are constructed to model nonlinear relationships, with the single neural network exhibiting superior performance in accuracy (average R² > 95%) and generalisability (RMSE < 0.1). To address the lack of interpretability in conventional neural networks, the permutation importance of variables is assessed to pinpoint the primary sources of bias and to clarify the mechanisms of variable interactions. The automotive company’s practical validation demonstrates the model’s capability to predictively assess the effects of abrupt alterations in bodyside dimensions on rear panel matching quality. The close agreement between predicted (e.g., 1.053693) and actual (e.g., 1.01) values confirms model accuracy, diminishing the reliance on supplementary quality control resources. This study provides a traceable, data-driven framework for enhancing quality control in complex manufacturing assemblies.