Talking about Industrial Automation with Rick Layman and Ginny Foster

What is the video of?

It's us. This.

Oh, I understand [laughter].

So what I would like to do is start off talking about you and your background and some of the projects you've worked on and then getting more specific about some of the problems that you solved for customers of Neff Power.

I'll give you an example. Okay. So my journey started off with the degree in electrical engineering, and leading up to and then after graduation, I was involved in a series of projects. And as I worked on these projects, as a team, we got better and better at defining the problem and then defining the solution. And it got to the point where we were able to do this in such a way that the solution was always new, unique, and nonobvious. And those three characteristics, new, unique, and nonobvious, those are the three characteristics of patentability. So my name is on five patents spanning three projects. And my favorite part about these projects has always been the interaction with people in the field. So bringing the technology out of the lab and to the people in the field. And so, after graduation, I wanted to know, is this a real job? Is this a real thing? So I looked it up, and I found out that it was. It's called Sales Engineer. And so I tailored my resume to match that of a sales engineer, and it worked. I got my first career job in the heavy manufacturing industry. I started at an international foundry, and I'm so grateful for my experience there. I'm so grateful for my coworkers and my mentors because they truly taught me so much. And it's because of their knowledge that they shared that I'm here today. I find that what I did in heavy manufacturing absolutely applies to industrial automation and what I focus on today. So today, I help connect people with engineers who specialize in industrial automation and robotics.

Okay, that makes sense. All right.

So tell me about your career journey.

All right. I started out in a maintenance department at an automotive supplier. It was injection molding, primarily, and there were some low-pressure molding and fiberglass molding? Yeah, I guess so, for headliners and stuff. And so that's where I learned robotics and PLCs. So I would spend a large amount of my time improving on the machines, changing processes sometimes. Sometimes just adding validation sensors and stuff, since it was primarily an assembly facility. But some things were more process related, such as water jets. There was a sewing machine, robotic knives, all kinds of cool stuff. Lasers. Lasers are always cool. So that was how I got my engineering foundation build, was working on those types of equipment. After that, I spent six years or so as a machine tool builder for extremely high-volume, fast-cycle-time, metal-machined parts that primarily service the automotive sector.

But there was some medical, some consumer paints, tees and elbow fittings, things like that, where big consumers are big buyers of that product. Those are very expensive capital machines, and there was a lot going on. They're 16 spindle CNC machines at high volumes. It was a tough, fast engineering role. My primary duties there started off as the robotic integrator, integrating about 10 robots a year, and then I did that. And then I also started doing full CNC integration machine, visual studio, front end, custom drivers, and all kinds of stuff. So there was a vast amount of things I did there to support that product. Then, I went to automotive manufacturer, big global automotive manufacturer for a period of time and was a senior controls engineer for them and helped do some stuff online, make some improvements for equipment going in, and helped oversee projects where people are installing new equipment. And now, I'm at Neff Power as an applications engineer.

So you talk about installing robotics and doing the integration on the robotics side, I found that there's that initial threshold. There's a lot of trepidation to adopt the first robot. And then once the company adopts the first robot, usually, it's a lot easier to see the scalability and see the repeatability. And after that point, the threshold for adoption is much lower, and there's a pattern of accumulating multiple robots in order to solve problems. And I'm wondering if you can speak to that trend.

There's always a barrier where people-- an intimidation barrier with new technology. I see it with all kinds of stuff. Internet, IP, people are used to hardwiring it like, "It's not broke, why change it?" Well, there's benefits to it. So I don't know. Robots definitely fall in the intimidation barrier for some people.

So Rick, what do you see as the benefits of adopting robotics to solve manufacturing problems?

The biggest benefit of using a robot is time to implementation. There's a couple of phases, but on the planning side, it's time to implementation or engineering time is drastically reduced. If you have the right reach, the robot can basically stick whatever part wherever you need to put it. There's not too many hang-ups there. But let's say you were to compare that to a Servo Gantry system, even a three-axis gantry, you can do that fairly practically. But to do that at certain time rates, you have to do a lot more motor studies, you have to do-- the amount of detail is greater there. The amount of specific parts is much greater. It takes a much bigger engineering burden to develop a system like that rather than to mount two-meter-reach robot on a pedestal. So that's a big benefit to me.

In terms of robotics, Neff Power usually talks about the differences between industrial and collaborative, industrial being high-speed, high-velocity pinch points, and collaborative being human collaborative, human and robot interaction, side by side, in a very safe way. And I'm wondering if you've seen anything, in particular, leaning one way or the other in terms of preference for the type of robot used and then the type of system used to operate safely with humans.

I would definitely say people are picking up more collaboratives, but they're still lagging, from what I've noticed, quite a bit behind in popularity. Collaborative is good, they work well, but they're harder to implement because you have to get more safety ducks in a row because, typically, let's say, either the operator is working right with them or they just want an open area. Usually, they just don't like cages is what I've noticed. So then you get an open-area robot, and that's not as bad sometimes. But if you just cage off, let's say, a general-purpose robot, a regular industrial robot, you can get going a lot faster. You also get, as you mentioned, better-- not better. You didn't say better cycle times, but you mentioned they're faster. So you can get more throughput out of those. I've seen a lot of applications where a collaborative robot would simply not be fast enough under any circumstance. I had customers specifically ask for them, and I tell them, "They're not fast enough for what we're doing. We can't do that." So that's a big hurdle for them.

In terms of the types of problems that customers are looking to solve right now, what are the applications where you're seeing robotics used the most? Is it palletizing? Is it pick and place?

The labor market is in a weird spot. What people seem to be automating the most are maybe the simplest tasks. Those are things like stacking boxes that anybody that's healthy can do. And so palletizing is really common right now. And it's not a very sophisticated task. Most people, even new people to robotics, can understand that without too much crazy hurdle, if they have a simple palletizing situation. And just a couple of vacuums on a robot is not really high tech in these days. So that's something a lot of people can handle, and they have the labor used to justify it currently. That's a really common one. The pick and place one, moving things from here to here or sticking it in this assembly, that's really common as well. There's unloading machines. A lot of people are starting to automate the loading and unloading of machines because they can't get a person to stand there like they used to or now, since labor rates are rising drastically. They're evaluating the costs more closely and then considering those things. Those are big ones.

Yeah, I agree. Those are the big ones that I'm seeing, too. So there's a conversation that you and I had back in December. Actually, it was part of a bigger presentation on end-of-arm tooling. And this kind of segues really well into that topic because I remember you were talking about these different applications like the palletizing, the pick and place, and I kept on pushing for the favorite end-of-arm tool, and everybody's like, "No, Ginny, there is no favorite end-of-arm tool." So I'm wondering if you can reiterate, again, why there is no favorite end-of-arm tool and what the benefits of Neff Power application engineers offer for end-of-arm tooling selection?

Sure, there's definitely no favorite end-of-arm tool, and that's really because all the applications are different or come with their own struggles. So a magnetic gripper would not fit the plastic industry or whatever. There are simple reasons like that is why there's no favorite one. You may find, let's say within people that make refrigerators, have the favorite sheet metal gripper or something, but there's no one-size-fits-all gripper. Nobody's always buying the Gripper 3000 from whoever, nothing. You got to make them. You got to evaluate it.

Here's some additional information about end-of-arm-tool solutions from Neff Power:

Neff Power applications engineers bring, I would say, a good level of expertise to end-of-arm tooling. By joining our sales engineers on different sides of different customers, you get a lot of ideas. You see a lot of things done in different ways. That leads to a lot of creativity within the end-of-arm tooling discussions that we have internally. I think that leads to a lot of good ideas that we in turn-- I don't want to say copy customer to customer. That's not what I'm getting at. But you see good ways to pick up parts, and you see bad ways to pick up parts, and you're able to turn that knowledge into maybe you step it up further, and then you come up with a new gripper design. That's a big one. We see a lot of products, a lot of industries, and you learn things from all of them. Certain industries get stuck within doing end-of-arm tooling a certain way, and I think having an outside source like Neff Power sees like-- let's say you're in the automotive industry, but you always pick up cars a certain way. But now, you're trying to deal with dunnage due to labor sourcing or something, and then that is a great example for maybe the packaging industry.

So Neff Power being a third party does not belong to automotive, does not belong to packaging. So you're able to leverage the stuff that we're seeing to help you with your solutions. That's a pretty big one. And then I would say just experience. Since we're selling quite a bit of robots these days, we're almost always helping with the end-of-arm tooling that goes with that robot. So we're racking up a lot of experience quickly. We already have previous experience on all this, so we're getting really good at going through our simulations and providing that certainty that the customer would like that. Once we develop something, we've already at least simulated it, it's going to work, and then we have the field experience to say we know this type of gripper or this type of whatever works good for this type of part with the type of assembly or movement we're trying to do.

I love it. That's a great answer. And it never really occurred to me that we are truly a third party coming in offering expertise, because we've seen so many different examples of how something can be done. So it's not just like you're taking a cookie cutter from one place to the next. It's a truly creative thought process.

So usually, when I've done podcasts, there's a human aspect of it, because all podcasts are very intimate conversations between two individuals, and there's always this question of what is your personal mission? Do you have a personal mission that you're bringing to your work as an application engineer solving problems every day?
Mine is honestly to do the most interesting thing I can [laughter]. It might be a little selfish, but I usually just like the things that are challenging or interesting or things I can learn with. So I guess maybe help the customer, because maybe I'll be clever after I learn enough things. I don't know [laughter].

So what has been the most interesting problem and solution that you have worked on?

One of the coolest things I've ever done is a 3D bin picking. So that was lawn mower industry, and that was crankshafts, and that was like 3000 pounds of crankshafts in a bin. And they're all locked together. And then I just built two robots, that just shoved them into a machine all day. And that was a pretty big challenge because bin picking was extremely new then. This is six years ago. Not anybody is really doing this. It was just challenging, and the fact that the code is pretty thorough in there. So that was a pretty big learning curve for me. That was pretty interesting. The way to think about the data or the ways to manipulate 3D data and working a mechanical device through a bin just based on data points and then whole series of, let's say, collision risks and other things within the bin, this is just a big, long challenge. That was definitely one of the most fun things I've done. Other than that, some things with servo controls are always complicated. One of the things I've done is build a control system where you can get 150 servo axes up on a machine with interpolated motion in a day. They're not 150 interpolated, but, I mean, the machine can be parameterized in a day, and the thing can be running in a day. So that was a pretty big task, and it was a lot of fun by the time it was done. I don't know. Bunch of cool projects over time.

That's fun.

What's the funnest thing or the coolest thing or the thing you want to share, Ginny? What's one of your favorite projects? Yeah.

So I have absolutely enjoyed doing phone calls like this with my customer, who's many states away and who has a design. So he would tell me what time he was getting in, and I would make sure I was like, "Okay, you got your cup of coffee? Okay, let's do this [laughter]." So it would be me, Kody [design engineer], and him. We would have an hour conversation. We did this for, I want to say, seven or eight different projects, where we would spend the project in 3D. So we'd pull it up in AutoCAD, show it to him in 3D, and he would go through and have a list. And so Kody is spinning it. He can't write it down. So I'm writing it down, and the guy has had coffee, so he's talking really fast, rattling it off. I confirmed back to him those are the changes he wants [laughter]. I mean, this takes anywhere from 30 minutes to 45 minutes, but we had a really good thing, and then he retired. But I got to meet him before he retired. I got to meet him in person. And that was really cool.

Cool. So you just really like discussing the projects with people and helping them.

Yeah.

Okay. That's cool.

Most of my work, I end up doing-- it's on the phone or now we've got Zoom, and we've got cameras and stuff. I've done now six podcasts. And my podcast talking points are usually the same. Actually, they're always the same, and I bring the note cards with me everywhere, just in case I end up having to do one. But I talk about the benefits of robotics, how they're always scalable, repeatable. They work lights out 24/7 because they don't take vacation and they don't get sick. So usually, with those three talking points, that's where everyone wants to bring in, "Okay, now that we have robotics, let's talk about artificial intelligence." So the advent of the new camera system, the one that you were talking about the--

PLOC?

Yes. How do you say it?

Well, I may be saying it wrong. It might be PLOC.

It's like P-L-O-C 2D [laughter].

Yeah. Yeah, yeah, 2D. Yeah, that's derived from-- they have the Inspector P series, so it's positioning location, I guess, and then 2D, because it's just a 2D camera in this instance. So yeah, that's the new SICK and Yaskawa. I don't know if that's a partnership, but little package they've come up with.

I think it's so cool.

It could be pretty helpful for customers because a lot of them-- you probably could have-- not probably, you always could have done it with any positioning camera from SICK, but there's a bigger interface hurdle, and that really tends itself more to the controls engineering side or understanding interfaces between machines. And we're talking about data here. So this isn't hardwired data, it's not a couple of inputs. It's positional data. So you need an actual data channel for this, and that's a weakness. A typical maintenance guy may not excel, something like that. So what's SICK and Yaskawa have done with that is kind of integrate that automatically into the system. And so what I'm getting at is it simplifies the interface. And I believe the interfacing is a part that people struggle with. So they kind of open themselves up to a new market just by basically writing a nice Ethernet IP driver for the camera, so.

So that's where I tell everyone that artificial intelligence is overlapping with robotics.

Yeah, AI within robotics will be cool. I actually had an INTS, the trade show in Chicago. One of the manufacturers had been picking, and I wonder if they scrapped it, but they were planning on integrating AI into it, and what that would have amounted to would have been great for me, because I set up that bin-picking cell, and I had so many roles, and I had to sit there for days and create averaging and what's my cycle time. And I did all these tweaks, and I would have to run it for a day and then go back and make tweaks and make tweaks. And it took me a long time to get all my numbers where I needed them to be. But with something like AI evaluating good and bad picks, and I could set up parameters that it could automatically adjust and try to change things, it would have done a lot of it for me and a lot more accurately.

It makes your job easier.

Right. And I was really excited to see that. I don't know if they came out with it, but it'll go there before long as bin picking becomes more relevant, especially because it's a big task, bin picking is. So having the robot discern if it fails in this region to block off this zone, and then that zone can be adjusted more accurately based on how well it does with certain numbers. That really automates the process. It automates the part that people struggle with, and it's time consuming. So things like that would make it better for a distributor like us. Basically lower the technology level where we can help with more simple things, the end user can get going quicker, and they don't need an engineer standing there for 10 days. So we're manually creating an algorithm. I'm really excited for the AI to help with things like that. Basically, the extremely high-level-- I don’t know but high-level engineering tests, but the things that just take a long time, a lot of tweaking, a lot of trial, machine goes a lot quicker with the right algorithm.

The people that were, I guess, manually adjusting these bin-picking cells before, they can be the people creating the AI algorithm, and then the people doing the implementation just don't have to spend as much time or don't have to be as technical. So it opens up a whole sales market, more or less, where before you used to maybe have to involve an integrator, or maybe they had to have a higher engineer on staff that was really good at technical or really good at vision. It simplifies a lot of that, and it gets you to what would probably be an effective sell much faster. That's why I look forward to. And also, they do it more simply, too. You can do a part recognition AI. This isn't something so obvious, and I don't even know if this really counts as AI, but they label everything AI. One of the cool things you can do is-- so when you have a camera and you look, if the part is over to the side, it is larger to the camera than if it is in the middle due to the way you see things, the way the camera sees things, the fact that the pixels are further away to the sensor now. So some cameras have things built in where you can feed it multiple images and then average those into a composite image rather than you as an op-- not an operator, a vision setup person, whatever term, having to set that up maybe eight different patterns, or do some extensive testing, there's a good chance that composite image would help you. So that's one small thing I've already seen AI improve in vision, specifically. I don't know if I'd call it AI. It's really just another algorithm that does it, but they labeled it AI, so it's pretty cool still.

I think it counts as AI. I count it anyway [laughter].

Thanks for having me on the show, Ginny.

Absolutely [laughter].

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