With on-demand expertise and visual guidance delivered through AR technology, Harpak-ULMA can support end users from anywhere and introduce new capabilities like virtual factory acceptance tests. Meanwhile, end users can use the technology to reduce operator learning curves and on-boarding times.
Creating New Insights
Harpak-ULMA has made significant progress on phase two, and is now underway on phase three, which involves incorporating more IIoT touchpoints. This essentially combines packaging machines with their digital twins to offer enhanced operating experiences and information integration.
“Expanding the breadth and depth of embedded IIoT lets us to integrate with advanced IIoT applications that expose detailed machine operations in real time to production staff,” Roach explains.
For end users, more data means more real-time, contextualized production information is available to both staff and onboard diagnostics.
“In this stage, we’re expanding on AR capabilities to deliver a deeper set of real-time capabilities that will allow staff to have what we call X-ray vision,” he says. “Exposing the digital twin of a machine is an interactive way to learn more and diagnose better.”
For example, if technicians get alerts about machine problems, they don’t need to shut down the machine, go through lockout/tagout procedures and open the control cabinet to investigate the problem. Instead, they can just use an AR headset, smartphone or tablet to look at a digital twin of the machine and identify the fault.
The fourth phase of Harpak-ULMA’s digital transformation is to apply machine learning and artificial intelligence (AI) to the vast amount of data collected during production. This effort, while still in the planning stage, will allow the company to create new business models by introducing cloud-based predictive maintenance and benchmarking services.
“This final stage represents an evolutionary step for customers in terms of their OEM relationship,” Roach notes. “Big data solutions that use machine learning and artificial intelligence will make the holy grail of predictive maintenance analytics in our markets attainable.”
For end users, predictive maintenance can help reduce unplanned downtime, as well as restructure legacy maintenance and cost models. And industry wide benchmarking offers them an opportunity to get a new understanding of the effectiveness of their production processes.
Of course, making these concepts a reality in production environments will come with challenges.
“We’ll need to resolve data ownership concerns and work through connectivity issues that arise with predictive maintenance monitoring,” Roach admits. “But it’s not like there’s no precedent. If this can be done for aircraft engines, it can be done for a packaging production environment as well.”
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