Few-Shot Vision-Language Inspection for Chrome-Plated Nail Clippers Using CLIP-Adapter and SigLIP2/T-IDEA Cache Adaptation

Industrial chrome plating for small metal products suffers from rare but costly surface defects, process drift, and a shrinking base of expert operator knowledge. This draft summarizes a KDT smart-plating case study using Royal Metal Industrial source materials. The work combines real-time process sensing, rack-level traceability, camera placement planning, and few-shot vision-language inspection for nail-clipper surface defects. Source materials describe seven process stages with temperature, pH, current, and voltage monitoring; rack identification using QR or barcode labels; pre/post-process visual scoring; and a staged deployment path from operator recommendation mode to automatic process control. The machine-learning component begins with a CLIP-Adapter baseline and then frames a stronger SigLIP2 plus T-IDEA multimodal cache adapter for few-shot anomaly detection and defect-type classification. The existing project report indicates that SigLIP2+T-IDEA improved few-shot performance by more than 15 percentage points over a CLIP baseline on a 22-image test set, while the initial CLIP-Adapter notebook produced 14.29% validation accuracy on a very small three-class example. These findings support the feasibility of cache-updatable vision-language inspection, but also show that anomaly thresholds, composite defects, and industrial lighting remain open deployment risks.