Visual quality control increasingly relies on machine learning (ML), but reliable production adoption requires a governed data-model-service lifecycle rather than an isolated detector. This article presents an implementation-grounded Open Access Platform (OAP) blueprint and audit protocol for visual quality control as a service. The contribution is not a new detector backbone. It is a reproducible governance pattern that connects ontology-based data validation, Neo4j knowledge-graph traceability, Eclipse Arrowhead service discovery, human-in-the-loop annotation, dataset and model registries, operational quality gates, and controlled model adaptation. The platform context is supported by implemented OAP tools for battery carbon-footprint analysis and production planning, while the visual inspection modules are reported at the notebook level. The benchmark protocol uses leakage-safe splits and OK samples with empty label files, separates the MVTec AD augmentation corpus from the unlearning stress-test corpus, and requires FP-on-OK gating with a retrained post-removal reference. The resulting architecture supports semantic traceability, controlled promotion and rollback, human accountability and Industry 5.0-aligned industrial AI governance without overstating the completed evidence. Compared with existing MLOps architectures, OAPs add inspection-specific semantic enforcement, knowledge-graph lineage, and governed adaptation.
Wołk et al. (Fri,) studied this question.