Overview of Edge AI in Manufacturing Industry
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 digitizing using AI technologies.
In the last few years, manufacturing industries have invested considerable money in machine learning-based monitoring systems. However, it is impossible to monitor each processing unit on a large scale for component failures and anomalies in production lines.
Manufacturing sector is increasingly adopting the RPA. Robotics plays an essential role in manufacturing automation today. Click to explore about, Manufacturing Process Automation
One significant hurdle in achieving this Digital Transformation is the latency in analyzing manufacturing operations on the cloud; Edge AI promises to solve this problem, making the process intelligent, efficient, and secure.
What is Edge AI?
Edge AI is a class of ML architecture in which AI algorithms process 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 existing systems. Let’s see some of the key advantages of Edge AI in general.
Operational Efficiency
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Significantly reduces latency, enhancing real-time decision-making capabilities.
Enhanced Security
Local processing increases the level of security in terms of data privacy-
Data is no longer shared in a centralized cloud.
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Secure Processing as data is not sent to the centralized cloud.
Decentralization of Workloads
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Decentralization of the Processing makes all the distributed systems efficient and self-sustained.
Manufacturing Industries have now begun implementation of Manufacturing Automation with the help of RPA to reduce human errors. Click to explore about, Top 5 RPA Use Cases in Manufacturing Industry
Edge AI in the Manufacturing Industry
Edge AI has considerable potential for building the smart manufacturing industry. One of the crucial components of 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.
How will Edge AI work in the Manufacturing units?
Edge AI has a simple architecture in which processing units can deploy a pre-trained 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.
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Data Ingestion: This stage will ingest the real-time sensor data from the machines' monitoring systems into the pipeline.
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Storage of the Data: The stream of the data will be stored locally and securely as no cloud processing is involved here.
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Processing: This stage of the pipeline will process the data according to the needs of the pre-trained models.
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Analysis: Here the data will be analyzed by the models, and they will give the results.
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Results: This stage compiles the results. After these results, the stakeholders can take the response.
A critical component of modern manufacturing operations, providing real-time data and analytics to support decision-making and improve the efficiency of production processes. Click to explore about, Enterprise Manufacturing Intelligence Tools
What are the Use cases of Edge AI in the Manufacturing Industry?
In this section, we will see the use cases of edge AI in Manufacturing
Predictive Maintenance
| Description | Benefits |
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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 more efficient to implement. |
Real-time fault predictions Helps in building advanced breakdown strategies. Cloud-less predictive results. |
Condition-based Monitoring
| Description | Benefits |
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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. |
Condition Monitoring can help increase the revenue as less cost is needed for maintenance. Condition monitoring makes the processing of these manufacturing more agile. One can utilize multiple streams to achieve the decisions as processing is on edge. |
Precision Monitoring and Control
| 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. |
Distributed processing. Making Industry 4.0 more achievable. Direct the manufacturing unit more autonomous and sell-tolerant machines, aka smart machines. |
Industry elaborates operations dedicated to producing tactile products shaping factories using robotic minds and display manufacturing. Taken From Article, Smart Manufacturing and Automation Service
Why to choose XenonStack?
XenonStack possesses the experience to develop and deploy such out-of-the-box solutions. These solutions require a team of highly skilled and experienced professionals. XenonStack provides you with a dedicated team for the development of customized solutions that help you fulfill your business requirements the way you want.
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