Episode 138 – Aaron Royster, Group Manager – Automation, Schunk: Audio automatically transcribed by Sonix

Episode 138 – Aaron Royster, Group Manager – Automation, Schunk: this m4a audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.

Speaker 1:
Hello and welcome to this episode of Manufacturing Matters, where we talk about the technology and trends that are shaping the global manufacturing industry. I’m Erin Hand with tech, B2B marketing, and I’m here today with Aaron Royster, who is, uh, sorry, I just screwed that up because I’m looking at the wrong copy. Okay. One more time. Can I hit it this time? Hello and welcome to this episode of Manufacturing Matters, where we talk about the technology and trends that are shaping the global manufacturing industry. I’m Erin Hand with tech, B2B marketing, and I’m here today with Erin Royster, who’s group manager of automation at Schunk. Welcome, Erin. Thank you for joining us today.

Speaker 2:
Yeah, it’s a pleasure to be here. Thank you Erin.

Speaker 1:
So, um, just to get us started, I just want to make sure our viewers know a bit about Schunk. Can you talk about, um, what you all do and, and your place in the robotics ecosystem in particular?

Speaker 2:
Yeah. Of course. So it’s a good place to start because our customers often associate schunck with one of our kind of silos of where we work. But in reality, Schunck is a manufacturing technology company. Any of our customers, you know, we, we are as close to the work piece as we can be. We’re gripping, holding, manipulating work pieces during manufacturing, assembly, all sorts of different processes. So we’re really excited to understand at a very deep application level what manufacturers are doing in their processes. And we support that with innovative technologies. And yeah, it’s really an impressive development that we’ve seen in the quick math 80 years since the company has been around. So we’ve seen many technologies come and go. And when we look at robots specifically, um, we like to think of ourselves as a robotic enabler. We help them actually pick up and hold and manipulate the work pieces so we can add swiveling, sensing, changing technology. So in the robotics ecosystem, we’re really proud of being able to support the robotic manufacturers and how customers actually deploy those.

Speaker 1:
Okay. And that’s, you know, I’m very excited about this conversation because I, I think you are such an enabler and such an important direction. You know, I think about the human hands and what we’re able to do with that. And, you know, the progression in robotics of being able to handle a lot more than the robots used to be able to handle. So, you know, I guess, um, if you could talk a bit about how, how those demands have shifted in the marketplace and how that affects how you go to market in terms of your, your development in the face of robotic evolution.

Speaker 2:
Yeah, that’s a really good question. Um, it’s an, it’s an interesting time right now because the last 3 to 5 years have led us to a lot of change. As an innovator, we are always looking for ways to move forward. But, um, it’s not just the, the manufacturing demands, it’s also what the kind of macroeconomic situation is driving us to, you know, the population’s aging. We’ve got people that don’t want to do manufacturing jobs anymore. So we’re looking at how can we take technologies and solve problems that are changing as well. Um, when we look at industries that are, that are changing and becoming more, um, sorry, I did mess up. I had a, uh, I’m cheating for the record, so I had a, uh, slide behind it and I was trying to click and because Ali had clicked on the screen, I couldn’t progress. So. Aaron, is it okay? Would you mind asking the question again? Because I didn’t hear specifically what you were saying because I was trying to get my computer to work.

Speaker 1:
Oh, okay. No problem. Um, I feel like I, I waffled on a bit. So, um, I’d like to leave that previous question, but I’ll kind of just give you a simpler version of it. So Amanda, just for the editing, I’d like to leave it how I phrased it before, but, um, so, um, Aaron, if you could talk a bit about just how the evolution of robots overall, how, how that affects your work in terms of meeting the demands of the industry.

Speaker 2:
So it’s an interesting question. Robots have been around for decades. The deployment of them is still an interesting thing. So you’ve got the companies that have adopted them in mass and are really successful, but we see a lot of the small to medium SMEs, those companies that are on the fringe, they’re trying to make decisions. The deployment and the available technologies in the ease of use space has been a really good starter for them. And what we’ve seen, it’s it’s kind of like a gateway into the world of robotics. So that’s one thing that we definitely have leveraged and helped people understand. Once they get comfortable with it and they get a cobot as an example into their space, they learn what it brings, where it maybe has opportunities and then that they, they take that what they’ve learned and they deploy it into a more, um, maybe industrialized solution in the long term. So we have had to adapt how we bring products to market to have those ease of use things right up front, as well as having the robust, reliable solutions that have been around for decades. Those are still widely adopted and relevant today. Um, so yeah, it’s, uh, yeah.

Speaker 1:
Okay. So when you talk about ease of use, is that partly your need to address a changing workforce that might not be as technically savvy and need need more. Um, not sure the right way to phrase it, but just much more user friendly.

Speaker 2:
I think there’s yeah, there’s definitely like the extremes. If you look to the far end of the advanced manufacturing engineers and they are deploying systems and they want the most advanced data collection, they want sensing on board, they want positional data, they want force feedback. That’s like the one extreme. And then you go to the other extreme where you’ve got somebody that’s deploying their first robot, and they just need to be able to open and close the gripper on their work piece and do some task with it. So we’ve definitely had to, to, um, cater to both of those areas. And we do that through things like software development or what we call application kits, where we make sure that a consumer can buy a piece of equipment that has all of the components they need. So again, when I think of those extremes, we’ve got customers that want to purchase a robot, a gripper and just do basic automation or we’ve got, you know, in-house people that have a robotics engineering staff and they deploy these on their own sites. And then of course, integrators that are deploying these cells. Turnkey for customers as well. So yeah, we have to run that full gamut of, of support of, of how do customers engage with and use our, our products?

Speaker 1:
Okay. All right. So, you know, one thing I think about is in a lot of ways, robots are really becoming more standard and, and commoditized in, in various ways. Um, so how does the end of arm tooling set that technology apart?

Speaker 2:
It’s a tough question. Um, so how does it set the technology apart? I like to think of it a bit more like the application groupings are what drives it. So like as an example, when we think of product development, we typically don’t take a build it and they will come approach. We typically look at where is the where might there be a shortcoming or a gap in the market and how could we solve that? But when I think of robots becoming more commoditized, um. It’s, I’m struggling a bit with it because the end of arm tool can be built today. Like we have special engineers at Schunck that design and build end effectors that may be starting from a blank sheet of paper. Um, so we really the end of arm tool can be built around those application groupings. So like we have a group that says, hey, we’ve got pick and place or we’ve got, um, man. Aaron, I’m sorry, that’s, that’s such a big question that I definitely floundered on that. I’m struggling with how to compartmentalize it.

Speaker 1:
It’s yeah, I mean, and maybe you’d like me to ask it in a different way too, or skip it all together. No.

Speaker 2:
So just like for a quick discussion between us, what What do you define? Like when I think of robots being commoditized, we’re. I still think we’re so far from that. But like the audience. What do you think that’ll mean to them? Do you just mean like it’s a. You can go into a storefront and there’s a distributor within 100 miles of you that likely sells a robot. Is that what it kind of means to this audience?

Speaker 1:
Yeah, I, you know, I’m not sure I, I, because I, and maybe I’m wrong about this. So maybe the premise of the question is incorrect. Um, I’m just thinking in terms of that robot arm, you get pretty much a standard robot arm. And I feel like it’s the end effector that’s going to make or break that application more of whether or not. There is there a different way you think I should ask?

Speaker 2:
I totally, I totally agree. I’ll have to be careful with how I answer it so I don’t upset the robot manufacturers, but okay, I think, I think that helps me more. It’s not so much on like the commercial availability, but it’s more on, hey, a robot is is kind of a robot. Is that more of the kind of stage? Right? Okay, I got you. That’s.

Speaker 1:
You know, whether or not you can handle a tomato versus a brick is, is so much up to that end end of arm tooling, right?

Speaker 2:
Perfect. All right, Aaron, I’m with you.

Speaker 1:
Okay, okay. You want me to re-ask. I’ll re ask the question.

Speaker 2:
Sure.

Speaker 1:
Okay. So, um, in some ways, I feel like the the robots are getting a little more standardized and commoditized. You know, you’ve got a robot arm that moves the way it needs to move. Um, but that end of arm tool or end of arm tool might be what really sets it apart in terms of whether it can get the job done. So I’m curious from your perspective of, of how that technology is in general, setting apart the robotics industry?

Speaker 2:
Yeah, that’s a really good framework because if you look at academic as an example, that makes, you know, single kilogram payload robots all the way up to FANUC moving around thousands of pounds worth of payload, it’s a huge range. And the end effector is like we talked earlier, it’s what’s engaging with and picking, placing, manipulating the workpiece, whatever that may be. So it really is what can make an application make or break. And it’s kind of interesting. What comes to mind as well is when we think about standardization and commoditization of robots, we often see that the end effector is thought of very late in the game. Everybody’s very concerned about picking the right robot with the right payload, with the right reach, which are all vastly important pieces. But I could give you several examples where we’ve had to go back and resize a robot, because we didn’t account for the mass of the end effector to accomplish what the application required. So, um, I think that’s really important is we typically try to start with the work piece. What are we handling? How is how are we going to achieve that goal and then work our way to the robot to make sure that we’re starting at the the core process and then working our way backwards? Like you hinted, there’s the robot market today is there’s so many good options from manufacturers that have been around for many, many years. There’s also a lot of newcomers that are bringing some good technology at a good value. So the market is is definitely very ripe with options. Um, we typically always revert back to start with what we’re trying to do and then go to the end effector, find out what that needs to be, and then go to the robot selection. So kind of inverting a bit from, hey, I’ve got this robot, here’s what I need to do. And then we get stuck in the middle trying to solve that problem with the gripper or some other combination of technologies.

Speaker 1:
Right? And that, that was kind of my assumption going in that you’re, you’re put in that position a lot of times, uh, of somebody decided on what robot they want and oh, gosh, how are we actually going to handle this product? Um, do you see a shift at all where that end of arm tooling could be driving more system architecture decisions if, if people bring you in earlier to these discussions?

Speaker 2:
I think it definitely drives it. I was trying to think through that really quickly. It seems so for sure. If you look at today versus ten years ago, when I think of system architecture, that could be things like PLCs or drive motors or controls that are controlling end effectors. Um, at least in the way that we think about it. And if you look at future proofing your system, you likely do want to plan for a lot of data capability and processing because we definitely see a shift towards, we call it intelligent, gripping, but servo driven mechatronic components that give you feedback different than a position sensor might on a pneumatic valve or a pneumatic cylinder, as an example. So I definitely think that the customers that we see successfully deploying and future proofing their systems are trying to look at what’s available today and how should we prepare in the future for what we build our system around? Because technology does change so fast. If you don’t at least try to future proof. You won’t be, you know, even close to accomplishing it in the end. The best laid plans are likely going to need some modification because nobody knows what is going to be available in five years, maybe not even 2 or 3 years as quickly as technology is changing these days. But yeah, that’s how I like to think of kind of system architecture. Architecture is look at what your need is today. Um, look at what you might have in your scope of, you know, we think about flexibility or varying work pieces. What might you expect needs to change? And then in the medium future, what might technology offer you that you could go ahead and prepare for, to have the right infrastructure to be able to control and do things with the data that you capture from these applications.

Speaker 1:
Okay, which, you know, the end of arm tooling. Technology itself, I think, is evolving so quickly. Just getting back to the whole notion of trying to replicate what humans can do with their hands, I, I feel like there’s been a lot of progress, at least in the years that, that I’ve been watching this industry evolve. Um, so how have you seen the technology continue to advance? And what do you kind of expect in the next few terms and a few years in terms of new capabilities?

Speaker 2:
So yeah, Aaron, you mentioned the, the dexterous technology and the question is interesting because in some ways it is advancing really quickly and in other ways, the technology is here and has been here. Um, when I think back to the early days of Dexterous Hands, Schunk actually launched a product that you could buy from the catalog back in 2014. It was a little ahead of its time. Um, but that innovation wave has been building for some time and now we’re seeing the hardware available. What I see as the biggest need and where my hope and expectation is around it really is the software. If you look at whether it’s dexterous technologies with many degrees of freedom or parallel grippers, whatever the technology is, the software has to be able to support it and drive the what you want it to do. Um, so when I look at, you know, these events like Nvidia GTC or, um, I’m drawing a blank on the event I went to, there was a humanoid conference that A3 put on last September. You go to these events and for what I take away from it is that there’s a really big need for companies and innovators to pair that hardware with the software, because the technology from the hardware side really is there. Now, of course, we have to figure out how to leverage it in a precise, cost effective, scalable way. That’s still we’re still working towards that in a lot of ways. But, um, yeah, it’ll be very interesting to see if I look at the last 3 to 5 years, I expect that pace will continue on. And, but I, again, I expect that the software and the ability to drive motors and positional, um, hardware based on changing dynamic environments, I hope that that’s where we go because again, that that hardware has been around for a decade at this point.

Speaker 1:
Okay. So really, you know, so if you, you bring up software and I, we can’t have a podcast discussion these days without bringing up AI. So, um, let’s talk a bit about that of how Schunk is leveraging AI within your portfolio or how do you see the potential of AI? Um, in, in how that technology works.

Speaker 2:
So if it’s okay, since we were talking about it, I’ll start with the kind of extreme option, which is what I would love to see. And we’re working on ways to accomplish this is in that hand example where whether it’s ours happens to have 20. So we’ll just use that as an example. Being able to use artificial data to control 20 degrees of freedom based on changing work pieces. So if things something’s coming down a conveyor and it’s not in a fixture and you don’t have a known position, there’s a really interesting opportunity to have a system learn the best way to grip that, um, using that information because you it’s important, obviously for applications where the work piece might be varying, you know, there’s vision systems that have been around for 30 plus years that are awesome at, I’ve got this work piece and maybe it’s controlled, maybe it’s presented chaotically. But it really seems like where we can leverage AI is in controlling these these hardware technologies when there are other considerations. So like one way that Schunck has done that is with we call it an application kit and it’s called smart grasping. It’s basically just a gripper with a camera. It doesn’t matter what camera you use, we just happen to provide one in the kit. But we, we did leverage an Nvidia processor as a GPU to use a language model and identify work pieces and the best way to grasp them.

Speaker 2:
So that’s one way that we use it because in electronic components or areas that might have qualified surfaces of metal, as an example, when you grip them, sometimes there’s areas where you do and don’t want to grip them. And again, if the work piece is in a controlled area, you can. Okay, I’m going to go to this position every time because the work piece is in the appropriate orientation. But a lot of our customers don’t have that luxury, and they need to pick it in a certain way with varying demands. So we can use AI to make those decisions on the fly, rather than having to do it with point control on the robot program. So that’s giving us some it’s an ease of use system. So it’s actually a SKU that a customer could buy and self deploy. So it’s a nice gateway into automation. Um, there’s again, there’s really good companies that do amazing work with 3D bin picking that’s reliable and robust. And we partner with those companies on the hardware side. But if you look again, using that kind of gateway term, smart grasping is a way to kind of get your feet wet and get a really robust entry point into automating with AI.

Speaker 1:
Okay. So I mean, it just seems like there’s a lot of intelligence that’s moving more toward that, that end effector.

Speaker 2:
Yeah there is. And that’s actually I’m glad you brought that up because with using the smart grasping as an example, one of the most important pieces is being able to control that stroke. So like, just as a simple example, when we pick up something, we can move our hands in certain ways and we almost inherently preposition. As I was picking up this water bottle, we’re just naturally we get to about the right size we want to be at, and then we finish the the hand pick. With advanced gripping technologies, you’re able to take advantage of some of those efficiencies with AI as well. So that’s an important piece. If you can control your position of your end effector on the robot, um, you can reduce time and you can make those decisions using AI. So that paired with things like force, torque sensing, understanding, if you’re trying to do an assembly process with artificial intelligence, but you might be trying to put a pin into a bore and you experience more resistance than you’re expecting. Those are important flagships to say, hey, I need to stop and have my engineer assess this. So technology is really enabling the appropriate safe. Um, safety areas. As AI is, is moving forward so quickly.

Speaker 1:
Okay. You know, it’s funny because I, you know, I have, I feel like with my limited brain, um, I, what I think about is difficult, you know, different difficult propositions with, uh, an end effector is I always go back to the tomato example of, you know, just being able to pick up a tomato. But I think there are so many other difficult situations for those end effectors to be in. What do you feel like is driving the industry the most in terms of, you know, what are the really hard problems that you all are dealing with that maybe you’re not there yet. Maybe, you know, this is a really difficult problem in manufacturing.

Speaker 2:
Yeah, that’s a big question. Um, let’s start with the tomato example. Just because that’s an easy one. Traditionally we coming from a, we come from an automotive background as a, as a company, you know, decades ago. And, um, traditionally we’ve not played in that space. Um, but the tomato example is a good one because you can take a, a gripper and go to a position with a set of fingers and pick it. But what happens when the tomato is slightly large and then you accidentally crush it because it was a larger diameter than the prior tomato? So kind of piggybacking on that, the applications that we see that are kind of most challenging. Um, it’s really, it’s really broad. And the reason I’m processing is one of the groups that I support, they do precisely that. They take applications that maybe can’t be solved with an off the shelf product and a set of fingers and another technology and bring it together. What they do is they’ll typically start with really understanding what are you trying to accomplish, and then how can we leverage either existing technology or do we need to make something from from scratch? So the challenging, some of the challenging applications are one, where one’s where the the process is able to handle a variety of things. Um, I have an example in mind, but I’m not sure if I can share it. So I’ll try to be, um, uh, ambiguous, but if you have a process of you’re doing something and the one work piece is 20in and you need to pick that and place it into the process, the next one might be 14in in diameter. Those are challenging. And if it’s heavy as well, because you need force, but you also need a good grip on the material. Um, yeah. Aaron, sorry, I’m struggling a little bit because I have a, I have, I have a specific example that is very fitting, but it’s under an NDA, so I can’t like use.

Speaker 1:
Um, I can understand that.

Speaker 2:
And sorry, do you mind reminding me, I kind of gave up on my flow, but where is this? Um, which question is this on the list? Just to give me a reminder of where we’re at.

Speaker 1:
So I had skipped it earlier on. So it’s basically just the, uh, what we’re pushing the EOP innovation in the hardest right now.

Speaker 2:
Okay. Gotcha. Let me think about that one because. I actually, I didn’t, I used my answer from that one earlier on the dexterous hands about the balancing the, um.

Speaker 1:
If you want, I can go into, because part of what I’m getting at is coming up. Question about flexibility. And that’s what I imagine is a harder problem now than.

Speaker 2:
Got.

Speaker 1:
It, which I think you were, you were kind of getting into with the tomato, the variability.

Speaker 2:
Why don’t I yeah, that’s I think that’s a good segue. Why don’t if you, if you don’t mind prompting me again and I’ll kind of mention like, okay, I got it. Now I understand the flow.

Speaker 1:
Okay, okay. So, um, and I, you know, I think where you were going with your answer was good because then I was going to get more into that flexibility question.

Speaker 2:
Yeah. Sorry for taking the opportunity away.

Speaker 1:
No, no, it’s perfect because I think it was a good segue. I was just going to dig a little deeper. Um, so, um, I’ll ask again about, about just what’s pushing the technology the hardest right now.

Speaker 2:
Yeah. Perfect. And if you don’t mind, throw me the tomato one again, because now that I understand where we’re going, I can leverage that. It’s actually a good, good move.

Speaker 1:
Okay. Okay, cool. Okay. So, you know, just from my limited knowledge of what’s required in this industry, I think about things like picking up a tomato and, and maybe I’m influenced by my years of covering the food industry also. But, you know, I think, you know, how hard is it for that gripper to not smoosh the tomato? But I also think that there are probably a lot harder problems than that these days. I think that’s been a little more solved. So what do you see as as the applications or situations in manufacturing that are really pushing your innovation for that end effector these days?

Speaker 2:
Yeah, that’s you’re right. There are probably harder challenges to solve than the tomato, but it is actually a good example. Um, I like the way that companies like soft robotics and Uber and these other companies have solved the food problem. What they bring is flexibility in like Differing diameters of tomatoes. If we take that because they come in with a flexible grip, and then they go to their home position and they stop around that tomato without crushing it. But if you look at industrial equipment and handling pieces of metal that are typically heavy and need good friction or capture grips. If we pretend that that’s a tomato, we can make special gripper fingers that would form to a single tomato, and we would be able to pick it because we could go to a position and kind of cradle it. But the next time we went to a slightly larger tomato and we go to that position, we’re going to crush the tomato because we’re going back to our known position. So for varying levels of, uh, form factor and payload, those tend to be some challenging applications. Um, it’s interesting when we think of automation, I, my inclination is to think of robots, loading machines, but you can also flip it around and do automation of the work holding, which is a slightly different way.

Speaker 2:
So like if we think of varying pieces of metal, say, you know, 2in to 12in, you could maybe find an end effector that would be flexible with enough stroke to pick that. But you could also have the robot pick and place the material already loaded into a vise as an example, into the machine. So those are some sometimes the application will drive us either towards a robot with an end effector or more towards like loading the material after it’s already had some prep work done into it. So it is really a kind of a wide variety of ways you can automate using a robot. Um, but yeah, those, those are the challenges that, that the group here we have good experience in, but it’s always a unique, one of the variety of work pieces. What are you doing today? And also what might you be doing in the future that you want to try and account for in this existing tool, or the ability to change over to something easy in the future.

Speaker 1:
Okay. Yeah. And, you know, again, I’m probably influenced by my years of covering more consumer product area. Uh, but I feel like flexibility is really the name of the game. And maybe that’s true for a lot of different industries. And we talk about the automotive industry too. Um, so I think, you know, I’ll picture, you know, different things coming down a conveyor or, you know, different orientations, different sizes that you need to adjust to kind of go back to that software and intelligence side of things, right? I mean, it’s that how are you helping customers adapt to that demand for flexibility?

Speaker 2:
Yeah, it’s a big, uh, flexibility is a big term. And to some people it’s simple. Some, a lot of customers when they come to us and say, hey, I need a flexible gripper. They just mean stroke. They just want to be able to pick a small part to a big part. Other people. That means that they want a lot of data and the ability to grip to a certain force or to a position as they go through their process. But what we how we’ve looked at flexibility is really working our customer facing ecosystem around the ability to, to change out what you need. So as an example, one of the exciting things that really was a flexibility, um, enabler that was recent is just simply changing out the gripper fingers on a gripper. So we realized that a lot of times customers would buy multiple grippers because they needed to quickly pick up a variety of work pieces. Maybe if you put a piece of steel into a lathe and you machine it down, a pneumatic gripper doesn’t have the stroke to be able to grip the new tolerance or the new dimension. So you would just have your robot swap out to a new tool. But we realized we don’t necessarily have to invest in the expense of multiple grippers. Sometimes the application allows us to just simply change out the fingers. So we really look at do I need to change out the gripper? Do I need to change out the finger? Could I solve this with simply more stroke? Or is there a form factor requirement that I do need to actually have a different feature set about my fingers to grip a new character? Characteristic of the work piece.

Speaker 2:
So for us, it’s really about an ecosystem. But also it’s interesting, I had a customer recently that, um, one of our tools is a tool changer that you can have your robot manually or automatically swap out to a different tool. And they didn’t need to do that today, but they just wanted to equip it for the future. And I thought that was really wise because that’s forward thinking of, hey, I’ve solved my problem today. I don’t expect to need it in the future, but things change quickly, so I’m just going to equip it with it so that I’m ready when the time happens. So I think it’s an important just almost like a continuous improvement process at a company. You always are trying to find ways to make things better and more efficient. I like that mindset when I think of flexibility, because you may not know all the parameters today that you need to accomplish, but just having that forethought and how can I equip myself to make that, um, kind of adaptability possible and easier in the future?

Speaker 1:
Yeah, that’s, that’s, that’s a really good point. And I, I think, uh, you know, you don’t necessarily have a lot of forward thinking people there. Like how, how can we get this job done today? Um, you know, I think too about just that whole question of changeover, you know, whether you need to change out the whole grip or whether you’re changing fingers, it all needs to be done as quickly as possible, especially if you’re in, say, a high mix environment. Uh, production environment. So are you making changes or advances in terms of how quickly people are able to, adapt or even maybe they don’t need to change the tool. I mean, what, what changes are you making there to make it easier for, for people to work in these environments?

Speaker 2:
Um. So Aaron, sorry, let’s talk through the question just a little. Well, I’m sorry, do you mind asking it again? Because I, I’m a little lost. Sorry.

Speaker 1:
That’s okay. So and and and again, um, maybe this isn’t a good question to ask, but I’m mostly just thinking about changeover, um, of, of whether or not you need to change the, the gripper, um, how quickly you can get that done so that you can keep production moving.

Speaker 2:
Okay.

Speaker 1:
Does that make sense?

Speaker 2:
I think so. Okay, I got you.

Speaker 1:
Okay. So, um, let me just go into that again a bit. It doesn’t have to be a big answer. Um, so just in terms of, you know, thinking about that flexibility and what’s required. And, and you talk about, you know, whether or not you need to change the whole gripper or maybe you’re just changing the fingers. Um, and people’s ability, especially in a high mixed production environment to change over quickly is very important so that they can keep production going. So, so what are you working on at Schunck that, um, you know, makes that end of arm tooling easier or faster to, to change over or maybe makes it easier for it to not need changing over.

Speaker 2:
Got it. So yeah, we, if we look at kind of today and go back, we’ve, this has been a problem or a challenge in the industry for many years. The, the way that a lot. You see a lot of applications solved is you’ll multiply the quantity of grippers on a tool. Um, so as an example, if you’ve got a opt in and opt 20, you know, you’re putting in your raw material, you run a cycle and then you’re prepared to take the finished good out and immediately swap the next part in. We see some pretty good efficiency there. And that’s just it’s almost machine tending 101. It’s it’s pretty rudimentary. The opportunities are. Whenever you have again, those those changing fluid environments, you may have to change out that dual end effector or change out one of them. So again, if we think about the term flexible, if you can make as, as flexible a end effector as possible and not have to change over, we see a lot of, of drive towards that technology or that application target because the robot is fast, but it has to go to some sort of home position, drop off a tool if it needs to change.

Speaker 2:
Pick up the next tool. Um, and then go back to its position where it needs to do the work. Sometimes the machine cycle times are long enough that that’s not a challenge. Sometimes the machine cycle times are quite fast and so we really have to take. What are the requirements of the application? What are the targets of, of automating. Um, and see what, what the, the, the driver is, but again, how schunck looks to solve those problems is really what do we need to do to accomplish the targets? Do we need to put six grippers on a single end effector because the machine is doing six work pieces inside of a vise at once? And if you go point to point six times, that’s going to take some time versus if we pick all six at once and enable the machine to immediately close that door and start functioning again. So we really have to look at how can we deploy all of the tools in our toolbox to accomplish what the customer is trying to, to achieve?

Speaker 1:
Okay. All right. So I’m going to put you on the spot here. Not that I haven’t been putting you on the spot this entire time. Um, but if you could take out your crystal ball and just give us a view. You know, even just three years down the road, what do you expect from end of arm tooling that maybe it will be able to do that? It can’t necessarily do so reliably today. Can you give us a look into the future?

Speaker 2:
Oh, that’s a good question. Um, let’s talk about the easy one first about this, the humanoid hand. Everybody’s talking about this craze. So that’s the easy one. But I think there’s probably some other things that could be be talked about as well. So the I think you hit it, if I remember correctly. Did you use the term reliable or um, robust?

Speaker 1:
It can’t do reliably today. So maybe it can do it, but it’s.

Speaker 2:
I think that’s the, that’s the key is repeatability is always what we’re focusing on. And when we deploy a technology into an application today, you know, we work in 99.99%. That’s the, the repeatability and the trust level and the process that it has to the process level that it has to achieve. When you look at the advanced technologies, the reliability is, is still open. Um, so that’s where I see that the hardware is going to continue to match the expectations of industrial automation today. Um, but also we mentioned software. I feel like that’s likely where the, the low hanging fruit of it’s going to have to match the. Sorry, Aaron, I think I’m, I’m floundering with that one a little bit.

Speaker 1:
No, that’s okay. I would, um, I would just pause with, you know, give yourself a pause before you start talking again, but, but and then start answering just the software part again. Okay.

Speaker 2:
Okay. So on the software side, it seems that that’s where the, the biggest opportunity is to align the hardware and software meshing together, because there’s companies at Schunk and many others that are developing outstanding hardware. But you have to be able to use it in a in a way that can be deployed in mass. Um, so on the, the dexterous hand, you know, just getting that one out of the way. I don’t know if it’s three, 5 or 10 years, but I expect that as quickly as things are moving forward. Um, that that’s likely going to happen. But if I look at today, there’s a lot of ability for whether they’re existing users or newcomers to automation, there’s advanced technologies existing that aren’t deployed in mass, whether it’s because they aren’t aware of them or they’re not, um, there, there might be a fear factor behind it. You know, coincidentally, I should have mentioned this before now, but I’m sitting in the, the Co-lab at Schunck, which stands for Collaboration Laboratory. And that’s one of the things we do here is we remove those concerns or those barriers to automation. So there we see a lot of innovation happening with existing technology being applied in new ways. Um, things like handling carbon fiber as an example, you know, people are looking at how can I automate this today? Whereas ten years ago, we were still maybe doing processes manually and we can apply unique tools from that toolbox in different ways. Um, so yeah, I think it’s going to be for sure, the hardware in the industry and the software is going to have to continue to be innovated and challenge the status quo. But I think a lot of it is also going to drive around. What are users of automation willing to try next and learning those hard lessons and then applying what we’ve learned and going through that revision process. Um, so yeah, I think in the, in the short term, it’s going to be the, the human factor that makes the, makes an impact of people really leveraging and building amazing equipment and, and doing those, those hard tasks.

Speaker 1:
Okay. I do want to get a bit back to the humanoid aspect of you? You brought that up a couple times and I it seems like it would be kind of a different beast. Like, I don’t know if you have a separate division that is working on humanoid hands versus other types of grippers, but, um, is the industry where it needs to be to develop these humanoids? Um, in terms of that gripper technology that is a part of that, um, workings.

Speaker 2:
So the timing for that question is great because traditionally we’ve handled it as part of our automation ecosystem. And recently it was announced that chunk is actually doing a separate division of our company because of the unique requirements. It is such a it’s complimentary, no doubt, but it is a unique, um, challenge for the hardware and the software and bringing do you need modularity? Does everybody need a five finger hand? Or could you do some applications with a thumb and two fingers. You know, if you look at the reliability, the precision, and also the value proposition and the cost of these things, those are all things that we’re challenging and trying to understand. What does the market actually require? So it’s, it’s insightful that you ask that question because we see that it requires a kind of a unique skill set and somebody that’s very technology forward and looking at the status quo and how can we challenge that and be better? So I think that we’re on our way. But I would, I would guess that many companies that are developing humanoid robots would tell you that the hand is the most challenging piece to engineer. Um, so I do think that that’s the area of the most opportunity. Um, but yeah, it is a, it requires some unique equities for sure.

Speaker 1:
Okay. All right. Well, before we wrap this up, uh, I do want to make sure that there wasn’t anything I forgot to ask you anything you you want to be sure to add into this discussion?

Speaker 2:
Um, I think we’ve covered everything. Trying to think of. No. Aaron from my side. It was a pleasure. And, uh, I enjoyed the conversation. And thank you for the consideration to have have us participate in the podcast.

Speaker 1:
I really appreciate you joining us today. I love that you took the time and I’m always interested to hear more about this technology. So thanks again, Aaron.

Speaker 2:
Yeah. And thank you as well.

Speaker 1:
Alright, so thanks to our viewers also for joining us today on Manufacturing Matters. If anyone has any follow up questions for Aaron, please feel free to put them in the comments, you know, on LinkedIn or YouTube or wherever you might be watching this. Um, you can also see past episodes of manufacturing matters on our website, manufacturing hyphen matters.com, uh, or your favorite podcast platform. So for now, do us a favor, hit like subscribe and keep tuning in. Okay.

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