\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 striving for operational excellence. However, most visible AI tools rely on the cloud for high processing power and scalability. Due to disadvantages such as latency, connectivity issues, security constraints, and cost, the cloud is difficult to use in industrial environments. The real-time feedback loops needed by production lines cannot be provided through the cloud.\n\n### Cloud AI vs. Edge AI Differences\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### On-Site AI is No Longer an Option, 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 to them instantly. Cloud connectivity 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 operating 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 fanless design for 24/7 operation.\n
\n\n### The Right Platform for Industrial AI: Emerson PACSystems\nThis generation of IPCs provides the performance required for continuous operation in demanding industrial conditions and on-site AI tasks. They offer powerful processing capability without the need for fan cooling, and have a robust chassis resistant to challenges like dust, moisture, and vibration.\n\n
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- Long hardware life, allowing the use of the same certified hardware 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 monitor high-speed winding processes. 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 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 production, 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.
