Decision-Centric Virtual Metrology for Geometric Deviation via Foundation-Embedding Retrieval

Modern defense manufacturing and sustainment require timely engineering decisions (e.g., inspection triage, rework/accept decisions, and process adjustment) based on the as-built geometric quality. Advanced machining systems generate rich multichannel controller and sensor streams, yet accurate geometric deviation labels remain costly and delayed because they depend on downstream metrology. This paper introduces ChronosGD, a retrieval-based virtual metrology framework. ChronosGD predicts pointwise geometric deviation from multichannel time series data by: (1) retrieving the most similar historical process windows in a frozen Chronos-2 embedding space, and (2) transferring deviation information through similarity-weighted aggregation. ChronosGD avoids plant-specific gradient retraining during deployment; adaptation is achieved by refreshing a labeled historical memory as new inspected parts become available, while preserving traceability through explicit neighbor provenance.