Digital Twin Orchetration System For Industrial Manufacturing

This paper has specified a self-hosted, Kubernetes-orchestrated digital-twin architecture for a five-station discrete manufacturing line: a Live Twin/Scenario Worker split with a DAG-based process twin under frozen-snapshot semantics; a seven-category machine/twin data model informed by, though not conformant to, ISO 23247; empirically calibrated causal simulators for each of five station types, with an explicit Must/Useful/Omit variable discipline and calibration sourced from public PHM, NASA, UCI, and Kaggle datasets; an MQTT-based communication architecture with a fully implemented bidirectional command path on the CNC station; and a complete experimental protocol for synchronization, scalability, resilience, single-host feasibility, predictive-maintenance baselining, and model-fidelity evaluation. The related-work analysis situates this contribution against both managed commercial platforms and the Kubernetes-digital-twin literature, with particular care to distinguish digital twins that run on Kubernetes from digital twins of Kubernetes itself, a distinction the existing literature does not always make explicit. Quantitative claims about synchronization latency, scalability limits, and simulator fidelity are, at the time of writing, pending execution of the protocol in Section XI on a deployed cluster. In priority order, the future work that would most efficiently move this contribution from architecture-and-protocol to a fully evidenced result is as follows. •  Run the baseline synchronization measurement of Section XI-A first. It is the cheapest experiment, requiring no fault injection and no scaling, just the system running normally, and it immediately populates the first rows of the results table, which makes every later section less hypothetical. •  Run the model-fidelity comparison for the CNC station (Section XI-F) in parallel, independent of the cluster being under load; it is an offline statistical comparison between PHM-2010/NASA IMS and whatever the CNC simulator generates, and does not require Kubernetes at all. •  Run the scaling and failure-recovery experiments of Section XI-C, the most implementation-heavy item, requiring multiple simulated lines for the higher twin counts and a scripted pod-kill protocol; these are sequenced after the two items above. •  Revisit the Abstract and the contribution claims of Sections I-F and XIV once real numbers exist. Several hedges in this paper, "candidate contribution," "pending," are placeholders for exactly the sentence a real result allows one to write with confidence instead. Beyond the immediate experimental program, natural extensions include generalizing the process DAG to allow rework loops and parallel lines (Section IV-B); replacing the production-token abstraction with a full discrete-event simulator if a throughput-optimization research question is later adopted (Section IX-A); extending the bidirectional command channel from the single CNC station to all five stations (Section X-B); and, most significantly for the digital-twin claim itself, hardware-anchoring the CNC command channel to one real machine, named in Section II-B as the smallest change that would move the strongest single station from a digital model to a digital twin in the strict sense.

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