The manufacturing industry, which deals with machines and automation systems, is one of the major sectors contributing to the global economy. According to Business wire, the sector accounts for nearly 16% of the worldwide GDP. As the industry has a significant impact on the economy, it has always gone through technical advancements. Industry 4.0 has been getting lots of hype recently; it is about digitalizing using AI technologies.
In the last few years, manufacturing industries have invested considerable money in monitoring systems using Machine learning. However, it is impossible to monitor each processing unit on a large scale for the failure of components and anomalies in production lines.
One of the significant hurdles in achieving this digital transformation is the latency in analyzing the manufacturing operations on the cloud; Edge AI promises to solve this problem, making the process intelligent, efficient, and secure.
Click to explore the Complete Overview of Edge AI Implementation
Edge AI is the class of ML architecture in which the AI algorithms process the data on the edge of the network (the place where data is generated, i.e., locally) instead of sending it to the cloud. This very nature of the edge architecture makes it a perfect fit for reducing the inefficiencies in the existing systems.
Let’s see some of the key advantages of the Edge ai in general.
Discover the Difference between Edge Computing vs Cloud Computing?
Edge AI has considerable potential for building the smart manufacturing industry. One of the crucial components in Industry 4.0 is introducing intelligence “on the edge.” Mounting intelligence on edge will allow machines in the production units to make higher-level decisions, act autonomously, and give feedback so that stakeholders can detect flaws.
Edge AI has a simple architecture in which processing units can deploy a pretrained model on edge, i.e., near the data source. However, one should note that the model on edge will only score the training part where there is no limitation for the computational power.
The above figure gives the architecture for the edge ai in the manufacturing unit. The process has the following stages.
Discover about the The Top 5 RPA Use Cases in Manufacturing Industry
In this section, we will see the use cases of the edge AI in Manufacturing.
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Predictive Maintenance Refers to the ability to pre-emptively detect the failure of machines using machine learning predictive algorithms. Predictive Maintenance has been in the industry for some time, but it has also been difficult to implement. Edge AI can play the role of catalyst to smooth out this process as it can process the data near the edge, making it simpler and efficient to implement. |
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Manufacturing units face challenges simply trying to fetch the data from their machines, processes, and system. One of the biggest hurdles is that each manufacturing units have their data streams, now firstly, all of these streams, whether of use, are sent to the cloud. Then processing is done. If some initial filtering can be done, then only useful data streams can be utilized in the cloud or locally, this can be achieved with edge ai near the data generation streams. |
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| Description | Benefits |
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One of the main goals of industry 4.0 is to use the data from multiple machines, processes, and systems from the manufacturing unit and use them for smart controlling and making precise decisions in real-time. This precision monitoring and controlling system use a large amount of data Machine learning algorithms. Edge computing is a perfect fit for it as it can collect, aggregate, and filter the data used by the AI/ML algorithms. |
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Akira AI possesses the experience to develop and deploy such out-of-the-box solutions. These solutions require a team of highly skilled and experienced professionals. Akira AI provides you with a dedicated team for the development of customized solutions that helps you to fulfill your business requirements the way you want.