- ABOUT IMC
- IoT LIBRARY
- RFP PROGRAMME
Ericsson, AWS, Hitachi showcase 5G Smart Factory
- September 5, 2023
- William Payne
Ericsson, Amazon Web Services and Hitachi America R&D have joined forces to highlight the ability of current 5G, AI and automation technologies to transform manufacturing. The three companies have collaborated to showcase current private 5G capabilities at Hitachi Astemo Americas’ electric motor vehicle manufacturing plant in Berea, Kentucky, USA.
“The best news about this collaboration is that it is not about capabilities that will be available at some distant point in the future,” said Thomas Noren, Head of PCN Commercial and Operations, Ericsson. “These solutions can be deployed today in manufacturing and enterprise environments to deliver a range of early adopter competitive advantages. As global technology leaders, Ericsson AWS and Hitachi America R&D have shown how collaboration can drive innovation.”
The solution leverages Ericsson Private 5G together with the AWS Snow Family to provide private cellular networks that have enabled machine learning models within the Hitachi manufacturing complex.
Using Hitachi video analytics, real-time video of the component assembly operation was fed across the Ericsson private 5G network to help detect defects earlier, reducing wasted material and lost production.
The goal was to build, train and apply these models to enhance product quality on the manufacturing floor, marking a significant step in the application of multiple technology components in industry.
The team set up cameras on a live production assembly line that produces motor components for electric vehicles. Real-time video of the component assembly operation was fed across the Ericsson private 5G network to an AWS Snowball Edge device running Hitachi video analytics. By using 5G wireless, the trial installation was completed in three days.
For one use case – Defect Detection – the trial demonstrated that computer vision running on a private 5G network could simultaneously inspect 24 assembly components compared with one-by-one inspection using conventional approaches. Using high-definition 4K cameras, the computer vision configuration was able to observe defects at the sub-millimetre level – far greater visibility than would be possible with the human eye. The high throughput and low latency of 5G was key to uploading huge volumes of video data from the cameras to AWS Snow Family device for analysis, which helped streamline decision-making.
The trial demonstrated that the combination of 5G and cloud technologies is now making full-scale, global deployment of digital production line applications viable – from defect detection and quality inspection to robotics automation, real-time machine control, augmented reality and more.
Built on Ericsson’s 4G and 5G radio and dual mode core technology, Ericsson Private 5G is designed to enable a variety of use cases for indoor and outdoor environments and integrate with business operations, devices and applications. Ericsson Private 5G provides 4G and 5G connectivity through a single server dual mode core.
“We explored and validated new use cases enabled by private 5G to show how smart factories can already function,” Sudhanshu Gaur, Vice President of R&D for Hitachi America and Chief Architect at Hitachi Astemo Americas, said. “The combination of private 5G, cloud and artificial intelligence/machine learning automated technologies has the potential to revolutionise the way we manufacture products, and we are excited to be at the forefront of this innovation.”
Chris McKenna, Global Lead, Private Wireless at AWS, said: “While it’s long been anticipated that technologies such as 5G and video analytics could drive innovation in manufacturing, one of the challenges has been how to securely and reliably process that data to drive outcomes. By using the Ericsson Private 5G Network product, and running artificial intelligence and machine learning models on an AWS Snow Family device, we were able to demonstrate a reliable and secure connection to run machine vision inferences at the site to help detect defects earlier.”