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    This diagram shows how data flows from the production line into digital twin clients. Data from production floor devices is collected through standard industrial protocols, such as OPC UA and then structured and contextualized by the unified namespace (UNS). A message queuing telemetry transport (MQTT) broker distributes live data from the UNS to the Unity-based digital twin clients on compatible devices for accurate visualization of the production line. Time-series and SQL databases store historical data that Unity can access as needed. The supervisory control and data acquisition (SCADA) system obtains the same data as the UNS in parallel and operates on it before supplying its data to the UNS, offering additional process visibility and control. Cloud-based services like Amazon Web Services or Azure can be used to host the UNS, databases and other backend components so that Unity clients can access the digital twin securely over the internet.
    E Tech Group helps large global manufacturer use real-time data flows from the production equipment
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    Hands of robot and human connect artificial intelligence AI circuit board to virtual smart chatbot with a command for automation generate, learning technology artificial intelligence machine, AI.
    While AI-generated code should be used responsibly, it could change the future face of the engineering workforce
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    Abstracted from hardware, software-defined platforms eliminate the need for single-source automation and can run on any compute device
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    Computer engineer is typing console script code by hand
    Retrieval-augmented generation (RAG) tools help large language models improve code program organization units
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    Before employing AI for machine design or operation, make sure it’s the best option
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    Control, monitor and adjust processes through software interfaces, rather than rewiring or manually adjusting hardware

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