Koh Young's Joel Scutchfield on Smart Factory Connectivity

Estimated reading time: 14 minutes

Scutchfield: I think it all starts with the ability for us as systems providers to be able to communicate with each other, and we are clearly moving in that direction.

Matties: Is the challenge that the equipment is not universally linkable yet? Do they have to go out and buy a new line to take advantage of it, or is it something that they can retrofit?

Scutchfield: Obviously, the end goal here is that we will have this communication protocol in place so that you can achieve what we're talking about without having to be overly specific in the equipment choices you make. Now, the choices are a whole other subject, but I think you're absolutely right, Barry. At this point, we are still not quite ready with the complete machine-to-machine initiative. Again, it's one thing to be able to access our inspection results and post that in an MES tool created by a pick-and-place manufacturer, but do we really call that full connectivity? It is connectivity, but it's only a part of it; it’s not giving the customer the ability to allow for any automatic or even manual adjustments. Getting the communication protocol established is what's going to drive this forward quickly. That's the limiting factor now.

Everybody's doing their own thing. Everybody's picking their partners to develop some level of connectivity, but those solutions are very specific and only potentially applicable with certain combinations of manufacturers’ equipment. We want to move beyond that as quickly as we can, which is why we are supporting the IPC Hermes and CFX connectivity standards.

Matties: It looks like there's a lot of energy as we see the CFX participation level increasing daily. There are other languages out there but, in the end, it all must talk together.

Scutchfield: That's right, much like the effort to develop the common connectivity protocol years ago. I would equate this to something very similar to that effort, but on a much larger scale. I don't know if we want to call it a race, but there's parallel path development occurring. Aside from that, you have all the initiatives between suppliers like us and other industry leaders for mounters and printers doing the same. We are incrementally moving to the point where we can each talk with one another on a certain level. At some point, though, we're going to be able to communicate broadly once the common protocol is in place.

Matties: Let's talk a little bit about the SPI. I know this is an area that you’ve been in. There are a lot of different approaches to SPI, but I think your latest announcement was talking about capabilities that are pretty far advanced. Can you just talk a little bit about that?

Scutchfield: In general, we've seen a shift in the application of SPI as a tool and technology. We are moving it from simply understanding the data and results to a system that will tell you specifically what needs to be adjusted to eliminate a bad result and how to make that adjustment. Manufacturers do not want to analyze reams of data; they want to quickly identify the issue and then quickly know what is needed to fix it. Better yet, they want to have the system make the adjustment automatically for them.

As we go forward, we want to use AI and machine learning to make those changes automatically, but it all starts with accurate, measurement-based data. Koh Young’s process optimization tools that we're developing hit that mark. It begins with giving the engineer the ability to perform an automated DOE quickly by extracting a sample of printed boards before production starts. This DOE would take place in the new product set-up phase and delivers the optimum printer settings for speed, pressure, and release. Then, once that proper set is in place, it provides a tool to monitor results and trends, and then a trend analysis well beyond the standard X-shift, Y-shift, and rotation anomalies. It goes to the point where we're providing very detailed visual information for where trends are occurring with regard to volume, inefficiencies, and more—what potentially is causing that issue and how you can correct it very quickly. The final piece is utilizing the machine learning and the AI engine to automatically assess the information and make changes as needed without human intervention.

Matties: The more data you capture, the smarter the system becomes.

Scutchfield: No doubt about it. That's where I go back to the whole concept of “true 3D.” The system must accurately capture a statistically relevant dataset of parametric, quantitative information the right way. Just like anything else, the more data you have, the better the result—assuming you have developed the tools for quickly analyze. We hit this mark every time.

Matties: I think we're in the age of data overload, so it's critically important for manufacturers to cherry-pick the data that they need and disregard the rest as noise.

Scutchfield: Exactly. There's so much data out there now, and we know this better than anybody. It’s just the nature of the beast. True 3D measurement creates much more information than pseudo 3D and definitely more than 2D. There are many more important parameters in 3D measurement, and we know both sides of big data. We must use the systems and our AI engine to help our customers extract meaningful information as quickly as possible, and then help them implement the result. Alternatively, we must implement the necessary changes automatically.

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