Edge Computing Nodes in Manufacturing: Benefits in Real-World Applications
Today, the world’s most advanced manufacturing sites use new technologies, and edge computing may prove to be the most promising. Briefly touched upon in our last blog “The 3 Largest Manufacturing IoT Use Cases,” edge nodes refers to distributed computers that process, analyze, and store data as close as possible to where the data is generated (in other words, closer to the devices and machines themselves) without having to send the data to the cloud.
Edge computing is still a work in progress, but in the age of industry 4.0, it can and will likely play an important role in the rise of smart factories — a term that expresses digitization in manufacturing. By incorporating edge computing nodes, latency is eliminated, bandwidth use is minimized, and even security risks are reduced. All of these are factors that matter to businesses including those within the manufacturing industry.
Edge computing fits into the manufacturing context by allowing industrial users to leverage data close to the edge to boost productivity, enhance quality assurance, reduce downtime and operational costs, and more. This blog will look into real-world examples of edge computing nodes in manufacturing while highlighting the benefits of edge computing that are applicable outside manufacturing too.
Edge Computing Nodes in Smart Dust Collectors Enables Real-Time Remote Monitoring & Bolsters Efficiency
As a $1.3 billion industry, the pharmaceutical sector calls for the use of dust collection equipment to continuously monitor and manage dust levels at the manufacturing level. Risks such as combustion or product contaminations cannot go unaddressed especially when pharmaceutical (pharma) manufacturing must adhere to certain safety standards.
It is not just the monitoring of dust levels in pharma manufacturing that is made easier with IoT; by adding a dust collection sensor, staff can reduce manual checks by a large margin and receive alerts to empty a container, thus ensuring the pharmaceutical plant operates more efficiently. While these are general IoT benefits, the incorporation of edge computing can facilitate even more efficiency by decreasing the cost of hardware as an edge platform allows for multiple functions to be converged on one device.
Furthermore, as with many large, multi-million dollar corporations, pharma manufacturing plants are often located in remote locations. Edge computing allows for the real-time remote monitoring of dust with data processing and analytics happening much closer to the smart dust collector. As highlighted previously, this advantage means less reliance on connectivity to a centralized cloud, with a network that may be overloaded by data coming from the dust collector sensors.
Edge Computing Nodes in Heavy Machinery Reduces Downtime, Potential Failures & Response Time
In this new era of automation for manufacturers, new technologies like IoT and edge computing are creating disruptive changes as manufacturers seek to automate their facilities. For example, the production of large industrial products, or heavy machinery manufacturing, reap tremendous benefits when integrating automated equipment into their assembly lines. Certain repetitive tasks like welding, material handling, and inspecting can be streamlined through the use of “robotic arms, sensors, conveyance, guided vehicles, artificial intelligence, and machine learning to improve quality, traceability, efficiency, and throughput.”
In scenarios where sensors are used to automate these tasks, it is likely that data is sent to a centralized system. With edge computing, manufacturers can automate the manufacturing floor and assembly line by having sensor-to-sensor communication occur closer to the source, rather than sending data to a server where it may slow down automation.
Heavy machinery manufacturers are not only gaining in-depth insights into how their machines are performing but also gaining the right data to respond to any technical problems, unscheduled downtimes, or other system failures. As highlighted in our last blog, predictive maintenance and analytics has also proved to help reduce downtime through the use of IoT. Now with edge computing, new applications can be enabled for sensors and other IoT-based equipment to detect potential problems in the earliest stages when predictive analytics is applied.
Edge Computing Nodes in Military Equipment Keeps Security Risks at Bay
It is not just physical risks that are abundant in the manufacturing field; there are also security risks that manufacturers need to mitigate. For instance, there are very real data security concerns in military contract manufacturing. Sensitive manufacturing data can include intellectual property rights, manufacturing know-how (including design and technical data), and other keystone materials. For the military, this data must be protected at all costs. And as a result, there are many edge opportunities to be considered.
While the defense industry is already utilizing IoT technology to “connect ships, planes, tanks, drones, soldiers, and operating bases in a cohesive network that increases situational awareness, risk assessment, and response time,” the military applications can go beyond, especially with the addition of advanced edge computing.
Consider a military contract that seeks to introduce new IoT-based surveillance equipment. Any unprocessed data relating to this project would likely be stored in a third-party cloud, which presents security concerns for the proposing organization. However, the use of edge computing may actually increase data security in this case.
For one, when using edge nodes, less sensitive data from the surveillance equipment is transferred to the cloud, thus reducing cyberattacks in transit. Additionally, without a centralized system, there is no single point of entry for hackers to exploit, so if one component is hacked, the server system will remain stable. Most importantly, data privacy can be maintained as data can be processed locally on an edge node.
Security risks cannot be completely eliminated with edge computing, but for military contract manufacturing it presents an attractive alternative to traditional security systems.
Edge Computing Nodes in Facial Recognition Better Supports Machine Learning
Edge computing solves the high speed and connectivity issues often constraining manufacturers’ IoT projects, especially when applying AI and machine learning processes. Take one example of a manufacturing plant wanting to incorporate facial recognition to identify those entering the plant, ensuring only authorized personnel have access. As mentioned in the previous section, there are physical threats to manufacturing plants too; high stakes industries like the military may require applying facial recognition technology for security purposes.
To achieve high performance, real-time monitoring for facial recognition, manufacturers must move to edge servers. This greatly improves performance, particularly as it relates to detection latency, frame rate, resolution, and so on — thereby making edge nodes effective for the use of machine learning applications.
It is important to keep in mind that some computing nodes with limited processing and memory capabilities may not be able to handle complex machine learning tasks. Therefore, working with end-to-end platform providers like SDT to optimize and enable machine learning processing at the edge is highly recommended.
SDT has helped one smart retail client improve vision detection accuracy in real-time at one tenth fraction of the original cost. Using a combination of an edge device and vision recognition engine with on-device AI, the client successfully launched a robot barista that welcomes customers void of latency issues.
Conclusion
There is no doubt that IoT at the edge is one of the top enablers currently accelerating the digital transformation of businesses. Particularly within the manufacturing industry, the range of edge computing scenarios utilizing industrial IoT devices is growing rapidly.
As edge computing evolves, so too will the role of the cloud. Accordingly, businesses should leverage cloud-based technologies to keep with the complexities that may arise. As a cloud and edge computing leader, SDT is prepared to customize your cloud service to meet your industry’s IoT needs as technology trends evolve.
With a complete and comprehensive understanding of hardware and software across industries and IT technologies, SDT offers various solutions that empower all types of IoT edge projects.
Currently, SDT is working on several high powered edge computing nodes for various use cases, whether businesses need a smart machine controller or AI enabled edge data processing. Also in production at SDT is an immersion cooling system for servers called AquaRack. Learn more about these developments by following our blog!
About the Author: Karen is a passionate B2B technology blogger. While studying at Georgia Tech, Karen first grew interested in cybersecurity and has since worked for several security and cloud companies as a global marketer. When she’s not freelance writing, Karen loves to explore new food trends.
