Ericsson, Amazon Web Services (AWS) and Hitachi America R&D enabled the private 5G infrastructure trial 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,” Thomas Noren, Head of PCN Commercial and Operations, Ericsson, says. “These solutions can be deployed today in manufacturing and enterprise environments to deliver a range of early adopter competitive advantages.”
The solution leverages Ericsson Private 5G side by side with the AWS Snow Family to provide the private cellular networks that were foundational in establishing machine learning (ML) models within the Hitachi manufacturing complex.
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.
“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, says. “The combination of private 5G, cloud and artificial intelligence/machine learning automated technologies has the potential to revolutionize the way we manufacture products, and we are excited to be at the forefront of this innovation.”
By using 5G wireless, the trial installation was completed in three days.
Chris McKenna, Global Lead, Private Wireless at AWS, says: “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.”