In the field of high-end precision manufacturing, quality control in production processes has long been challenged by both spatiotemporal data sparsity and error lag. Traditional offline sampling methods struggle to capture the dynamic fluctuations in production, while single-dimensional feedback controls fall short in addressing the nonlinear coupling between multi-dimensional process parameters and final product quality. To address these challenges, this paper proposes a production quality management system based on multi-dimensional data learning and an active error elimination method. First, to tackle the issue of sparse sampling, an Adaptive Gaussian Process Regression (AGPR) algorithm... More >