#286 - Data, Discipline and the Future of Inkjet: How Droptimize is Redefining Jetting Performance

In this episode of the FuturePrint Podcast, we speak with Raphaël Wenger, co founder of Droptimize, a Swiss engineering company bringing a data driven workflow to one of the most complex corners of industrial inkjet: waveform optimisation.

Wenger shares the origins of Droptimize, which grew out of years of hands on optimisation work at the iPrint Institute. The manual process of logging variables, testing parameters, and tracking results was slow and error prone. Droptimize was created to automate that workflow and give engineers reliable, searchable access to all waveform and jetting data. Today, the company provides both optimisation services and drop watching instruments with integrated data management.

We explore the challenges of industrial inkjet development, from the sheer number of parameters involved to the difficulty of working at high frequencies and long throw distances. Wenger discusses how Droptimize has enabled customers to unlock new performance levels, including a recent automotive printhead project where Droptimize identified a completely new waveform that is now in commercial use.

The conversation also covers broader industry trends, including the rise of data driven development, increasing interest from ink manufacturers, and the movement toward automated or self optimising workflows. Wenger gives insight into emerging applications such as robotics based direct to shape printing and the long term potential of bioprinting and tissue engineering.

Looking ahead, Raphael sees three major trends shaping the future of inkjet applications. First, direct-to-shape printing is gaining momentum and often involves long-distance jetting—a technology that needs optimized waveforms to maintain print quality over extended printing gaps. Second, high-viscous jetting is emerging, and these applications often rely on multiple pulses to shear-thin the ink until jetting is achieved. When combined with direct-to-shape, this will enable the use of inks similar to paints for decorating complex 3D objects. Finally, he sees long-term potential in biomedical applications, an emerging frontier where inkjet technology could play a transformative role in tissue engineering. The scalability of inkjet is particularly well suited for this, as it can print very fine structures—such as blood capillaries—at dimensions matching those of living tissue.

Raphael also previews his presentation at FuturePrint Industrial Print in Munich, where he will demonstrate new Droptimize capabilities including misting analysis, high frequency stability testing, and the company’s nozzle navigator for rapi

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Transcript

SPEAKER_01 0:02

Yeah, hi there. Welcome to uh today's FuturePrint podcast. And I'm very pleased to have Raphael Wegner, who is uh working for a company called Droptimize. Droptimize, very interesting company that are joining us in Munich. Raphael, good to see you. Good to be talking to you today.

SPEAKER_00 0:21

Hi Fraser, thanks for the nice introduction. Uh so yeah, my name is Raphael, and um I co-founded Droptimize a few years ago with my associate Florian Bourgem. And basically, the story was that uh we wanted to improve the workflow to develop waveforms and overall improve the jetting performances in production.

SPEAKER_01 0:44

Yeah. So the the And you've done that, haven't you? You've basically done that.

From Bioprinting Roots To Industry

SPEAKER_00 0:48

Absolutely, yeah, yeah. So to tell you the story, basically, Florian myself have been working in uh inkjet research and development uh at the iPrint Institute for roughly five, 10 years before we started Droptimize, and there we've been facing those challenges that uh people would meet in production, so mostly industrial partners, and trying to find a solution for the print quality to be what they wanted, for the performances of the printers to be what they want those. And we found this process to be very heavy uh because there are so many variables in the process, and that's basically what we looked into. We tried to make it smoother because it was just too much data for for handling on a daily basis for optimization.

SPEAKER_01 1:35

So you so you just to go back, you have been in Inkjet for quite a long time, haven't you, really?

SPEAKER_00 1:42

Yeah, a bit more than 10 years now, that would be 12 years now, yes.

SPEAKER_01 1:46

Yeah, so so at the point where you started in Inkjet, then Inkjet was not in its early days, but it was relatively early in adoption. You know, it was being adopted, wasn't it? But it was kind of still testing certain different segments. And what were you working on right at the beginning?

SPEAKER_00 2:07

So my my first topic uh at iPrint at the time, this is where I really started Inkjet was uh bioprinting.

SPEAKER_01 2:14

Oh, really?

SPEAKER_00 2:15

So this is uh where my background actually is. I studied biomedical engineering. Kind of uh an engineer with some sensitivity to to what's biomedical applications could be out there. Uh and um I started working on those topics, and then over time I switched to more industrial applications, which is where I'm at today.

SPEAKER_01 2:38

Yeah, and uh and dropimize as a business came out of an idea, really, wasn't it?

Spreadsheet Pain And The Big Data Pivot

SPEAKER_00 2:45

Absolutely. The thing was that um basically I was doing optimizations on a daily base with those Excel sheets and trying to get around what I did yesterday and be sure I have the right data and everything written down and take some screenshots of what I see. And I found this process to be very, very bothersome because, to be honest, I'm not the best at keeping track of what I'm doing. So, because every time you do a test, you would need to write down plenty of things like the frequency you're printing at, the jetting distance, the temperature of the ink, maybe the room temperature, maybe the humidity. Uh, then you need to have all the information about the waveform, and that's basically 10 to 20 parameters you need to write down every time you do a test. And I found this to be too much effort, yeah, and um also easy to lose track of. So that's when we decided to make something that handles this whole part of the job for you that just keeps tracks of everything and can do these tests automatically for you.

SPEAKER_01 3:48

Yeah, yeah, yeah, yeah. So that's basically the story.

Automated Drop Watching And Databases

SPEAKER_00 3:55

Exactly. Basically, what we did is we brought big data to uh printing optimizations. So it's mostly through drop watching, it's mostly about the waveform, but we also look at what's around the printer, so the ambient temperature or the ink temperature, the flow rates, those kind of things. And the way we differentiate is that we can test plenty of sets of parameters, so we can test plenty of waveforms, and everything is automatically recorded. So it goes in a database straight away, it's automatically analyzed and stored, and it's available forever in there. So whenever you are not sure about what you did yesterday or about or six months ago, or you want to compare something, you can just open that previous test and you have all the data available. Yeah, and it's in there, it's managed for you. You don't need to do anything.

SPEAKER_01 4:42

Yeah, and this is really useful for people who are developing technology but who also got technology to kind of fine-tune, isn't it? Really?

Waveform Sweeps And High‑Frequency Stability

SPEAKER_00 4:54

Exactly. We uh use it to basically test many possibilities of for typically it's the waveform. Yeah, for example, we do sweeps uh of let's say pulse duration or amplitude or pulse spacing or different shapes of waveforms, yeah. And we can just test hundreds of them, and this really increases our chances to find the right optimum in that data set. Yeah, and we can do also comparisons in conditions that are close to what you will encounter in production, so we can do tests at high frequencies and then really look at the stability and optimize in those conditions. So a typical application is, for example, the with the soundbacklint heads. Yeah, uh, you need to do some damping at high frequencies, and these are things that we can directly do at the high at the target frequencies, and we can optimize the waveform at 60 kilohertz uh and really look at what's happening there and find the right damping that will work at 60 kilohertz.

SPEAKER_01 5:50

Got it, got it. So um who typically are your customers? What kind of customers are they? You know, it sounds like you might work with integrators, but you might also work with the OEMs and the head manufacturers. So just give me an understanding of the your kind of your customer base.

Who Uses It: Services Vs Instruments

SPEAKER_00 6:09

Yeah, so maybe the first point is that we we do we offer two solutions. One are the services, yeah, and this is pretty much open to anybody. So there we have people that come from ink manufacturing industry, we have some collaborations with print head manufacturers. Um, I would say most of the customers are integrators, so these are people that have a printer that needs to print yesterday, and uh they come to us and we try to help them to get there quickly. We also have people uh that are in academia in some new topics where they need some exotic uh jetting performances, let's put it that way, and have some exotic fluids. And these are the the typical customers we have for the services, and now we also sell uh the drop watching instruments with our big data solution. Uh, and this is something we started quite recently, and there the customers are pretty much the same, actually, but it's more for people that need it on a daily basis. Yeah, yeah. I would say the the key differentiation between services and sales of instruments is how often you need it. Yeah, people that do it every day. I recommend to buy an instrument, then they can work autonomously. People that need it once in a while, I think it's better to lay it back on our experience and the hardware we have, and then it's just more efficient.

Building The Tool And Iterating With Feedback

SPEAKER_01 7:25

Yeah, yeah, yeah. I understand that. And and that financial model is quite good as well, isn't it? Because you you will have two different sets of customers, some who are kind of regular users, some who just buy the technology and use it themselves. So that makes sense. Um, and how do you just just for me to understand, how do you personally develop the technology? So so obviously you guys know what you're doing, but how have you developed it and how would you continue to develop it for potential challenges that might occur in the future?

SPEAKER_00 7:55

So we started kind of in garage mode, it was also COVID time. Uh so we were happy to do home office and develop the the early stages of the software at home.

SPEAKER_01 8:05

Yep.

SPEAKER_00 8:06

And then we started using it for the services, yeah. And then we we we used it, now we use it really on a daily basis, and we see the problems with our software. So, okay, say okay, um, our stability analysis is not very good, the results are not clear enough, we want to improve that, and that's how we do it. Basically, we use it, we see what is painful for us as an operator, as a user, yeah, and then we have the ability to solve it and improve it directly. And this is the solution we offer to the industry, yeah. And what is really nice now, also, is that we get the first feedback from the industry and we see what these guys are doing with our drop watchers, yeah, yeah, which is a bit different than what we were doing, and they have some different applications also, and this is also very insightful. This allows us also to adjust uh to improve the software according to what are the needs, the needs that we see out there.

SPEAKER_01 8:57

Yeah, yeah. Um, I'm always interested to know what you feel you've achieved and what kind of cool things you've done. So, give us a bit of an insight into that.

SPEAKER_00 9:08

So, sadly, I cannot give very concrete examples because as I think it's of everybody in Inks that everything is pretty much protected by NDAs. Uh, but some of the things I've done uh that were really cool are uh new printheads that are not on the market as of today's, but they should be soon. Uh, that we allowed uh that we we developed waveforms with for uh it was the automotive industry, and these printheads uh nobody could really find the right waveforms. Uh, even the printhead manufacturers worked on it and uh weren't exactly what they wanted. And there, I would say within a few days we found something that was a complete game changer. Yeah, so we went from multi-drop for long distance to single drops that had the right volume, the right speed, and uh that are now in production. These are products you can see in the streets driving around, and uh and this is our uh development inside. Oh wow, and this we can really be proud of because we we just took a very different approach with the waveform and it's worked out uh absolutely fine. It's a new standard, even for the printed manufacturer.

Data Platforms And Self‑Optimising Workflows

SPEAKER_01 10:16

Yeah, yeah. Yeah, that's interesting, isn't it? Interesting. Uh if you thinking forward, thinking into what might happen in the future, what's coming next, what is it you sense is going on in this market?

SPEAKER_00 10:34

So overall, I think uh I see a rising interest on data-driven data management, these kind of solutions.

SPEAKER_01 10:43

Yep, agree.

SPEAKER_00 10:44

Uh, it makes sense. We have requests from labs that want also to centralize all the data about, for example, a given ink to merge the data from the rheology, uh, from the printing trials, from uh the drop watching. So I see a trend going in that direction to really have data-driven solutions when you know what you're doing and you can retrieve the data and compare and not just navigate uh in the fog.

SPEAKER_01 11:08

Yeah, yeah, yeah. Yeah, great.

SPEAKER_00 11:10

So that's one of the things I see coming already happening, happening, I would say.

SPEAKER_01 11:15

Yeah.

Long‑Distance Jetting And Direct‑To‑Shape

SPEAKER_00 11:16

And on our side, what we're looking into is uh self-optimizing tools because we already have the data-driven workflow, and this is the first step if you want to do something that can automatically uh go through optimizations, is that you need a robust data path, a robust workflow around it. And this is something we've already established. So we have that workflow where we can automatically test things and get the results. And now what we're looking into is uh into replacing basically us as an operator by some self-optimizing algorithm that would be able to converge uh towards a good, uh well-working solution. I don't expect we'll be able to do the most advanced applications in the next couple years, but I see a lot of potential for people that are, for example, developing inks uh so that they could just press a button, get a working waveform, and just do a printing trial and look at the other properties they're actually interested in, too.

SPEAKER_01 12:14

Yeah, yeah, that's interesting. So, do you think in a way the the ink manufacturers may become customers for you?

SPEAKER_00 12:22

Absolutely, and actually our first roadwatcher is at an ink manufacturer, so yeah, yeah.

SPEAKER_01 12:26

Oh, yeah, of course, yeah.

SPEAKER_00 12:28

So these are people that do a lot of testing and they want a clear uh clear quantification, uh qualification of the inks. Yeah, and this is where we are really good at because we have this data-driven workflow, so you can just select two inks uh from our data from the previous tests, you plot them side by side, and you will see the difference. Yeah, you can look at the drop speed, the drop volume, the stability, the number of missing you see, all those things are available, and this is especially interesting for them because obviously they have a lot of inks and they try to improve those.

SPEAKER_01 13:02

Yeah. So drop my as a business, you really am I right with this, that you kind of very much in that sort of industrial inkjet world, the applications that inkjet are that are more industrial. What do you see as the sort of trends around the potential applications for inkjet?

Bioprinting Hopes And Technical Gaps

SPEAKER_00 13:25

So, what I see happening at the moment is long distance jetting as a development from our side, but it's more like applied to direct to shape, so kind of robotics 3D printing. Yeah, this is something I see coming, and from our side, from top in my side, it means basically long distance jetting. Yeah, so this is we have customers that are working on those topics that come to us uh asking questions about the right waveform to get those drops far away that you can actually print on non-flat objects. Yeah, and this is something we see. What I'm always hopeful to see, right, someday, uh, is the biomedical printing. Uh, I see a lot of originate, exactly.

SPEAKER_01 14:05

Yeah, yeah.

SPEAKER_00 14:06

At the time it was really maybe too much in its infancy, there were too many challenges to be addressed at the same time. Um, but I think really, I think on the one hand, yeah, the the inks are really complicated to achieve because you need something that is self-friendly, that is biocompatible and also printable, and somehow you need a way to make it harder to make it solid. So these are challenges from a biomedical point of view.

SPEAKER_01 14:33

Rafael, what what what particularly what you know what tell me an example of an application, biomed application, and you can see this possibly working in.

SPEAKER_00 14:42

So in gel printing in biomedical applications.

SPEAKER_01 14:46

Yeah, give me an example of one that you think could be something.

SPEAKER_00 14:50

What we so there is what we call tissue engineering.

SPEAKER_01 14:53

Yeah, that's what I was thinking.

SPEAKER_00 14:54

So tissue engineering is basically, yeah, you can think of it about yeah, it could be um the layers of your skin or some muscle or heart tissue, something like that. And there uh you have multiple scales. So the the overall object is gonna have a dimension of several centimeters, but actually, inside that big quite big structure, you need very thin structures like capillary blood vessels, yeah. And these guys are more like in the micrometer or 10 micrometer size, and this is where ink that is the best.

SPEAKER_01 15:26

Yeah, yeah.

SPEAKER_00 15:26

This scalability uh of injet is very unique, yeah. And there I would really see some potential. Yeah, I think that there is still work to be done on the on the inks. Uh, these are very special inks, and also the the printheads uh nowadays, as far as I see, there isn't really a perfect one for those kind of applications. You kind of need you need a good recirculation, you need it to be affordable, you need to be able to sterilize it, you need a low debt volume because these feeds are extremely expensive. Uh and these I haven't seen today. But if somebody out there has an idea about a printhead for those applications, then maybe. Absolutely, yeah, yeah. No, that could be maybe the the next revolution. I think not for today, but in 10 years, that could be something.

SPEAKER_01 16:12

Something. Um, just I think we should just mention your co-partner, so Florian. So it you and he were the the guys that kind of created dropy's. Um I'm guessing that you too will be both in Munich in January.

SPEAKER_00 16:31

It's not sure as of today, to be honest.

SPEAKER_01 16:34

Okay, but you definitely will be.

SPEAKER_00 16:36

I will definitely be, I will see, but maybe I will have some other people from our nearby network that join.

Munich Talk: Workflow, Missing, Nozzle Navigator

SPEAKER_01 16:41

Yeah, makes sense. Um, what are you going to be talking about in Munich? What uh what's your presentation gonna focus on?

SPEAKER_00 16:49

In Munich, I will start with just introducing our workflow, uh just to show to differentiate a little bit what we are doing from let's say what most people already know from dropping solutions on the market.

SPEAKER_01 17:02

Yep.

SPEAKER_00 17:03

And then I will dive into some examples. Um typical examples that we have at the moment with our latest developments are analysis of the missing and optimizations of the missing.

SPEAKER_01 17:14

Yep.

SPEAKER_00 17:14

So I will talk about these kinds of new applications we have developed. I will also talk about uh the nozzle navigator is a feature that allows us to check the condition of a whole printed. So we just move the drop watcher around all the nozzles and we can look at the stability within minutes.

SPEAKER_01 17:30

Yeah, yeah, brilliant.

SPEAKER_00 17:32

And then I will show some very nice examples of applications that are more like I call this more standard, but like stability in frequency, stability in uh temperature, stability uh in those kinds of conditions that we we do also.

Closing And Thanks

SPEAKER_01 17:46

Brilliant, brilliant. Listen, been great to have a chat with you. Thank you very much for giving us your time this morning. Um, it's a cool, crisp morning for all of us, and I know that you are heading off to go skiing because you're based in Switzerland. So in the winter, what do you do? But you go skiing, why not? Um, so Raphael, thank you very much for giving us your time. Looking forward to seeing you in Munich and uh yeah, drop to mise. Interesting. So thank you for a bit of insight.

SPEAKER_00 18:15

Thanks, Fraser. And uh yeah, and looking forward to seeing you very soon, yes, indeed.

SPEAKER_01 18:20

See you very soon. Thank you very much.

SPEAKER_00 18:22

See you very soon, bye bye, have a good day.