Edge AI is a change agent in manufacturing
Digital systems have become central to manufacturing. Intelligent applications using AI for predictive maintenance, quality control, process management and demand forecasting are increasingly being employed. Many of these use cases demand fast response and real-time interactions. That makes AI at the edge—on the assembly line, the plant floor, in warehouses and throughout the supply chain—a fundamental infrastructure component. Using common off-the-shelf technology and rapidly evolving software, edge AI enables manufacturers to make near-real-time, informed decisions, which can reduce waste and maintenance costs while increasing yield. And that allows operations to be more efficient, cost less and eventually run autonomously. Enhanced levels of AI-based automation help manufacturers withstand the challenges of fluctuating labor, demand, energy and supply chains.
AI at the edge enables critical manufacturing use cases
Many of the AI-powered digital manufacturing applications that will drive competitive advantage depend on consistent and capable edge infrastructure to supplement the cloud-based services widely used today. AI at the edge is required, as the amount of data generated will increase dramatically in this new generation of sensors, video and IoT devices, jamming the pipeline. This makes the haul back to the data center more time-intensive and costly. What manufacturers want from all that data is the ability to make better decisions in real time, but increased latency works against that goal. Manufacturing relies on latency-critical applications, some with sub-10 millisecond response times, to ensure safe collaboration between machines and humans. The bottom line is that cloud services are not up to the job by themselves; they need to be supplemented with the edge. And even if cloud services can meet performance demands for initial deployments, the constant growth of data and the continual need to support real-time interactions will drive the need for AI at the edge. In contrast to the cloud, AI at the edge can keep up with the speed of manufacturing, delivering the responsiveness to learn from constant streams of machine data and apply near-real-time intelligence.
Machine Vision Solutions: Detect and Prevent Defects
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Download NowManufacturing organizations demand that their investments in digital systems provide clear and demonstrable business value. AI at the edge will enable new use cases that deliver for the bottom line.
Predictive maintenance
Predictive maintenance systems have been in place for years, but they usually work from static data that is normalized across all processes. AI at the edge makes it possible to drive repair or maintenance actions for specific machines or units and data from them, but only when there is a clear need to act. That is because these actions will leverage the increasing amounts of machine learning data used for better decision-making. It will be a great advantage for process manufacturers to use AI to reduce interruptions and stoppages.
Automating inspection processes
Calling on computer vision, AI at the edge makes timely and frequent inspections possible so manufacturers can better identify process and logistics problems. AI can support inspections at various steps of the process, including preproduction, production and shipment. AI makes it easy to tie together all the inspection information from those steps, and even take action to resolve issues. This makes 100% inspection an achievable goal, rather than relying on statistical inspection models that can miss unique or infrequent errors.
Mobile robot support
Effective utilization of mobile robots demands processing massive amounts of data and making intelligent decisions quickly about where and what a specific robot is doing to ensure that it is acting as an effective part of the team. In many use cases, the robots must be managed in near real time, and this demands AI at the edge to reduce latency. Manufacturers will be able to leverage all the salient data to manage these devices. And as with nearly every AI use case in manufacturing, the number of devices, amount of data and need for intelligent evaluation will only increase with time; AI at the edge provides more headroom for expansion in the future.
Computer vision
Now that it is possible for digital systems to see, multiple cameras and inputs monitor entire processes, enabling intelligent applications at the edge to filter and act on that data. What’s more, AI-powered vision applications will support real-time interactions with robots and make it possible to physically coordinate them. Computer vision can also dramatically improve worker safety by identifying and stopping unsafe actions before accidents happen. Visual analysis helps ensure that operational standards are in place or that staff follow optimal procedures.
Intel delivers AI at the edge to power intelligent manufacturing
Delivering AI at the edge must start with a cost-effective approach that embraces existing data and operational infrastructure. It must be secured from the start that utilizes defense in depth, including zero-trust principles. Intel is providing a proven set of building blocks that empower consistent and secure infrastructure for AI at the edge for manufacturers and its many use cases. To learn more, please click here.
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