Issue link: https://iconnect007.uberflip.com/i/1504794
26 SMT007 MAGAZINE I AUGUST 2023 ing at a system. If you're simply automating what the engineer or operator does, you won't be successful with the digital twin. You need to be pushing what more information can be gar- nered from AI. However, for all the promises of AI and such, we're not there yet. We have to manually look at this process differently, and what makes sense for the machine. You know, when people first tried flight, they were mimicking the birds' mechanics, flapping their arms like birds. It took the Wright broth- ers to stop and ask, "What are we really try- ing to do? We're trying to fly. So, let's rewrite the laws of fluid mechanics and start to look at that better." at led to the thinking that, if we want to fly, maybe we shouldn't fly the same way as the birds. Perhaps we should use what we learned from the birds but fly differently. It's the same thing with automation and AI. We'll do the same thing. We can't just mimic the human by making a humanoid or an artifi- cial neural network when what we really want is to improve the entire process, including the design, automation, and delivery through the supply chain. In my mind, that's the vision of the digital twin. Matties: We're already doing a version of digital twin in design. With the digital twin of the manufacturing process, first you need to benchmark the processes, collect the (digital) data, and do it in real time so the digital twin is current and active. That seems to be the greatest challenge. Maybe they don't see the ROI in doing it. It's both. It's hard to justify the ROI. I'm work- ing with some companies right now, where they struggle with the question, "What's the payback?" My advice is that there won't be much immediate payback. If I put sensors in a furnace, for example, to better understand what's happening inside with temperature, and one sensor shows the temperature is off the set point, then you know that one part of the product will get a much different tempera- ture profile than the other. at might explain why you're getting a certain output that you didn't think you would get. It may help you understand the process but may not immedi- ately yield better ROI. It's hard to put an ROI on that. Now we have AI and machine learning which we can use to see trends that we weren't able to see before. I've struggled to model the processes that I've worked with, just trying to correlate into curve fits. I do all this stuff, and it's beyond me. But an AI will start to see pat- terns—when this, this, and this happens, then that tends to happen over there. You might think you can get some ROI on that informa- tion, but you can't justify the costs to sensor- size the system upfront. I don't know what those things are until I find them; I can't jus- tify what I can't predict. It's hard to do that, but that's what Industry 4.0 and the digital twin can help with. Matties: Phil, thank you for sharing your insight. My pleasure. SMT007 Phil Voglewede