Corrigible Structural Intelligence for Automated Multi-Branch Defect Extraction in CFRP Ultrasonic C-Scans: A Turing-Complete Discrete Topological Approach

The increasing complexity of carbon fiber reinforced polymer (CFRP) structures demands reliable, interpretable, and high-throughput Non-Destructive Testing (NDT). While deep learning offers high accuracy, it suffers from fundamental limitations in out-of-distribution generalization and "black-box" un-auditability, requiring massive labeled datasets. To address this, we propose a novel NDT framework driven by Corrigible Structural Intelligence (CSI) and discrete topological search. By mapping 3D ultrasonic C-scans into a combinatorial voxel graph, we formulate defect extraction as an energy minimization problem solved via heuristic annealing. Crucially, we introduce strict symbolic hard constraints—a Turing-complete topological operator set—that enforces 26-connectivity and completely intercepts “structural hallucinations” (false crack bridging across background noise). To overcome the computational bottleneck of combinatorial search, we implement a C++-level optimization using Numba JIT, achieving a 50x speedup for 500,000 state transitions. Experiments on the Swinburne CFRP ultrasonic dataset (post-lightning strike) demonstrate that the CSI-driven framework eliminates random core instability (yielding repeatable, single-truth outputs) and acts as an auditable structural filter that perfectly isolates true physical damage (branching cracks) from background noise. This work bridges the gap between structural philosophy and industrial NDT, providing a white-box, high-efficiency paradigm for next-generation structural health monitoring.