An intelligent SCNNBN-TBiG hybrid model for casting defect classification

Casting is a fundamental manufacturing process for producing components with complex geometries; however, surface and subsurface defects continue to compromise product reliability andproduction efficiency. To support automated and consistent quality inspection, this paper presents a hybrid deep learning framework termed SCNNBN – TBiG for intelligent casting defect classification. The proposed approach integrates stacked convolutional neural networks with batch normalisation to extract stable and discriminative spatial features, followed by a Transformer encoder that captures long-range contextual relationships through multi-head self-attention. The resulting representations are compressed using global average pooling and subsequently analysed by stacked bidirectional gated recurrent unit layers to model sequential dependencies within the learned feature space. The framework is evaluated on a publicly available industrial casting image dataset comprising 7,348 samples under both defective and non-defective categories. Experimental results demonstrate that the proposed model achieves a testing accuracy of 99.44%, outperforming several existing deep learning and hybrid architectures. The findings confirm that the synergistic integration of spatial, global, and sequential feature learning provides a robust and efficient solution for high-precision industrial quality inspection.