Designing Modular AI Infrastructure for Steel and Metalworking Operations: Separating Architectural Interoperability from Model-Level Generalization
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Nikita Vadimovich Elagin
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Modular architectures built from containerized microservices and standards-based communication layers deliver scalability and cross-plant portability to artificial intelligence systems in steel and metalworking operations. The interoperability these architectures deliver is a structural property of the deployment layer, separate from a statistical property: whether the AI models the architecture hosts generalize to a machine, material, or lighting configuration different from the one they were trained on. Container-orchestration benchmarks, cross-machine anomaly-detection studies, and steel-surface defect-detection results converge on one conclusion: a fully functioning modular deployment can host a model whose accuracy collapses outside its training distribution, and current reference architectures for industrial AI collapse the two properties into a single, undifferentiated claim of adaptability. Adoption decisions and vendor claims in this domain require two independent checklists, one architectural and one model-specific. Existing predictive-maintenance and computer-vision platforms already embed retraining pipelines that concede the model-level limit their surrounding language papers over.
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Authors
Nikita Vadimovich Elagin
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