\n\nIncreasing globalization, labor shortages, supply chain issues, and sustainability pressures present a complex picture for manufacturers. Manufacturers must improve quality, reduce scrap and emissions, and maintain efficiency, but the necessary experts and personnel are often insufficient.\n\nThe solution to these problems lies in digital tools. Artificial intelligence (AI) and machine learning (ML) technologies can bridge the gaps faced by companies seeking operational excellence. However, most visible AI tools rely on the cloud for high processing power and scalability. Due to disadvantages of the cloud such as latency, connectivity issues, security constraints, and cost, it is difficult to use in industrial environments. The real-time feedback loops needed by production lines cannot be provided via the cloud.\n\n### Differences Between Cloud AI and Edge AI\n
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- Cloud AI is unsuitable for most industrial applications due to high processing power requirements and latency.\n
- Edge AI processes data locally, reducing latency, lowering costs, and ensuring data privacy.\n
\n\n### AI at the Edge is No Longer a Preference, But a Necessity\nReal-time operational technology (OT) tasks differ from general information technology (IT) tasks. Real-time industrial AI cycles collect data, analyze it, make decisions, and feed the result back to the controller. AI instantly detects deviations and automatically corrects machines, virtually eliminating scrap.\n\nFor example, milliseconds are critical for cameras capturing 30-100 images per second on a production line and controllers responding instantly. Cloud connection delays can lead to scrap, downtime, or occupational safety issues.\n\n### The Right Processors for Industrial AI\nGPUs are effective in AI model training, but for AI running at the edge, i.e., in the process where the model continuously performs a task, specialized edge AI processors are more suitable. Emerson's siMa.ai MLSoC platform supports real-time closed-loop systems in industrial PCs and can make instantaneous corrections.\n\n
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- High-speed computer vision and large language model inference capable of processing dozens of images per second.\n
- Low power consumption and a fanless design for 24/7 operation.\n
\n\n### The Right Platform for Industrial AI: Emerson PACSystems\nThis generation of IPCs offers the performance required for continuous operation in demanding industrial conditions and AI processing at the edge. They provide powerful processing capability without the need for fan cooling, and have a robust chassis resistant to dust, moisture, and vibration.\n\n
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- Long hardware life, allowing the same certified hardware to be used for years.\n
- Uninterrupted and predictable performance.\n
\n\n### Application Example: Pipe Winding Vision System\nA thermoplastic pipe manufacturer switched to an AI-powered vision system to supervise high-speed winding operations. Human inspection was slow and prone to errors. The new system inspects pipes after winding, quickly transmits data to the controller, enabling immediate correction.\n\n
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- 100% inspection and reduction of human-induced errors.\n
- Adaptability to almost any pipe diameter.\n
- Scrap resulting from winding errors virtually eliminated.\n
- Video recordings support quality and compliance certifications.\n
\n\n### The Future of AI in Industry\nThis approach can also be applied in areas such as laser welding quality, automotive adhesive placement, life sciences packaging and controlled manufacturing, oil and gas leak detection, mining, and recycling lines.\n\nAI-powered industrial solutions are revolutionizing manufacturing by meeting demands for data security, real-time control, and energy efficiency.
