Research on Component Reuse Method in Case-Based Reasoning Based on Multi-Dimensional Feature Fusion

Deep learning has achieved notable success in 3D shape retrieval. However, precise component matching for reuse in intelligent manufacturing remains challenging. This is mainly due to the limited representational capacity of single-dimensional features and the inflexibility of fixed similarity metrics. To address this, we propose a multi-dimensional feature fusion framework based on Case-Based Reasoning (CBR), which integrates geometric, semantic, and point cloud features. Specifically, we design a Semantic-Guided Feature Fusion Mechanism that uses cross-modal attention to dynamically align heterogeneous features under unified semantic guidance, and introduce a Dynamic Metric Learning Network that adaptively assigns dimension-wise weights based on query–candidate pair characteristics. Unlike previous methods that rely on simple feature concatenation or uniform weighting metrics, our approach achieves deep cross-modal synergy and sample-adaptive similarity computation. Experiments on a dataset containing 661 mechanical parts across 5 categories demonstrate the effectiveness and generalizability of our method. This work presents a promising approach for component retrieval, with performance demonstrated on a limited dataset; further validation on larger industrial collections is needed to confirm its practical applicability.