Being an Engineer
Being an Engineer
S6E37 Duann Scott | Computational Design & The Best File Format for 3D Printing
Duann Scott is a globally recognized leader in computational design, additive manufacturing, and the emerging intersection of software and fabrication. With a background in industrial design and a PhD research foundation from the University of South Australia, Duann launched BITS to ATOMS in 2009 to explore how digital tools would revolutionize product design and manufacturing. What started as an academic pursuit quickly transformed into a dynamic industry journey through some of the most innovative companies in the space.
At Shapeways, he helped build one of the first online 3D printing communities. At Autodesk, he shaped the strategy for the $100M Spark investment fund and led the acquisition of Netfabb, now integral to Autodesk's digital manufacturing suite. At nTopology, Duann served in multiple executive roles, driving growth and expanding the company’s software integrations for advanced manufacturing applications.
In 2021, he relaunched BITS to ATOMS as a consultancy and launched CDFAM, the Computational Design Symposium Series. CDFAM now brings together cutting-edge thinkers across engineering, software, and architecture at events in NYC, Berlin, and Brooklyn. Whether supporting MIT xPRO students, contributing to the Wohlers Report, or guiding the 3MF Consortium as Executive Director, Duann is committed to building better tools, workflows, and communities around computational manufacturing.
Beyond his professional pursuits, Duann brings a creative edge from his past life as a musician and designer, continually pushing the boundary between art and engineering. His mission? To create a better digital thread from bits to atoms.
LINKS:
Guest LinkedIn: https://www.linkedin.com/in/duann/
Guest website: https://cdfam.com/
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The design process and the manufacturing process in a very abstract way, completely linked like that is inseparable. This is kind of like a very organic, abstract kind of almost art approach to this, which can then change the way we think about how we design an injection molded part, perhaps,
Aaron Moncur:hello and welcome to the being an engineer podcast today. Our guest is Dwan Scott, a driving force the world of computational design and additive manufacturing. He's the founder of cdfam, a global symposium series connecting engineers, designers and developers pushing the boundaries of digital fabrication. From his early days at sheepways to leadership roles at Autodesk and N topology. Dwan has helped shape how we design and make things. He's now leading the charge at the three MF consortium, bringing structure to the digital thread for additive manufacturing. Dwan, thank you so much for joining us
Duann Scott:today. Thank you for the opportunity. I appreciate it.
Aaron Moncur:Well, let's talk about cdfam. First, tell us, what does that stand for, and what's the story behind this. How did you create this?
Duann Scott:CD, fam could stand for anything really, but it started off, starting as computational design for additive manufacturing, and it was kind of as a joke, because, like, defam, is kind of hard enough for most people, so adding a computational element to it is like another step of complexity, but I think it's what's critical to realize the potential for additive manufacturing. So if we just go towards additive manufacturing design with our existing framework of how we design engineer things, we're not really typically going to get the performance and productivity gains you need to justify the cost of 3d printing things, which is typically more expensive than other ways. And so it started off as a bit of a joke, and then the the URL was$1 so when I was thinking of starting a design conference for your printing it was it was cheap, and it had easy SEO because no one else was using it. So I called it city fam. And then as the event has evolved, it's grown to cover computational design at all scales, not just additive manufacturing. And that happened very, very quickly, like within the first year I was scaling up to architectural applications of computational design processes, because there was such a pull from that industry and others. And so now cdfam Could mean computational design for artificial intelligence and machine learning. If that's what you want to be, got it talk
Aaron Moncur:to us a little bit about computational design. I mean, how do you define that? How should we be thinking about computational design? What are some examples?
Duann Scott:I'm sure there's some academic examples and some arguments that are going on, ranging in especially architectural academia, about what computational design really is. But for me, it's really about building the systems, including algorithms, to meet multi objective requirements for a parts performance and manufacturing or construction process simultaneously, and being able to weigh those different requirements against each other to produce not a single optimal design, because optimal is like a razor's edge, which, if you move across in any dimension, it breaks, but a robust design which meets all of those requirements that you then sort of fed into it via data to sort of build a system to make the objects. So you're not really just designing a single object or a single mechanism or a single part or a single building, but a system to solve the design decisions and then make things, whether it be one off or a series of things, in the, you know, in the world of mass customization,
Aaron Moncur:would general CAD programs qualify then as computational design? I mean, effectively, it's a huge algorithm, right? With a lot of custom inputs that output a design
Duann Scott:typical CAD systems, kind of linear in the programmatic way, you may better drive an Excel spreadsheet and do a design of experiments to sort of explore the solution space, but unless it's bringing in other data, about, say, simulation data of performance for. Or the manufacturing process, or its ecological footprint, you're kind of not bringing all the data together in one platform to solve that, that design problem there are. There slowly, there started to be ways that you can sort of drive data into the into the design process. But your typical sketch, Extrude, loft radius on on corner, isn't really computation design. It may be parametric, but it's not really bringing in data to drive things. It's sort of a linear process, which is has solved many, many, many engineering problems, and will continue to do so. But some of the more gnarly ones, it's just not up. This is not enough data to drive decisions to do as well as we can.
Aaron Moncur:Yeah, that's a good distinction. What are some examples of true computational design?
Duann Scott:We can look at it from from mult, from any from scale. So let's, let's start small. So if you're designing a medical implant, let's say a bone graft for a cancer patient, you're taking in scan data as a DICOM, you're dissecting it, be programmatically analyzing the density of the bone to know where to cut your programmatically putting in angles of where you're going to make a make those cuts. And then you're automatically making a jig to so the surgeon can use that to cut the cut the bone. And then you're generating the geometry which is going to replace it, which we're using as a ladder structure to increase in osteo integration and allowing for the load that's on the bone structure, it's going to happen as the person you know assimilates that titanium into their body without causing more problems. So you've got these multiple objectives you're sort of dealing with simultaneously, and you want to do it programmatically and repeatable, so that you're not sort of manually doing this every time you need to do an implant. The surgeon doesn't have these skills. And the typical, you know, mechanical biomechanical engineer, you know, they're expensive, so you want to sort of take their understanding of process and embed it into a system which is then reusable and appliable through different, you know, for different, similar but different applications.
Aaron Moncur:Yeah, are there any pieces of software out there that that you can recommend, or maybe not even recommend? Maybe you don't want to put your stamp of approval on it, but share that engineers, designers listening to this might be interested in checking out
Duann Scott:the backbone of computational design is really has been, for the past 1015, years, has been grasshopper within the rhino platform, by whatever quirk of The history of how code systems are developed and how this particular software came to be. It's a very open system which is affordable at a student level and has been taught in architecture for over 10 years. And so there's 1000s and 1000s of people who know, who have learned, particularly in architecture, how to programmatically design systems to make things. So because of that, we see a lot of the footwear companies you know, the major sportswear footwear companies that you all know, love and wear, hire architects as part of their computational design team to build these systems. And if you think of how, yeah, because they, they know these computational workflows from from doing, you know, building facades 10 years ago, and from facades to footwear, you think it's not that big a jump. No pun intended, because you're really just building a system which are then applying three different sort of requirements.
Aaron Moncur:How have you seen computational design evolve over the years? You know, kind of, where did it start, and what are the capabilities?
Duann Scott:Oh, there's, there's an academics who'll tell you where it started. I don't really know where it's evolving. Is from large scale down. So from, as I said, some architectural facades sort of like took the first edge, because you could do these cool designs. And people like Zaha had Did, did these undulating forms, which are algorithm driven. And then as the platforms matured, they started to use it for things like figuring out the HVAC system in buildings, and then, you know, calculating sunlight reflections, so that you don't make some sort of Archimedes death ray into a car park of a skyscraper. And, you know, burn people's buildings, burn people's cars, as has happened in the past. So. Using these calculational systems to predict performance of a building over time. And that sort of sort of led the wave. And then as it came down to smaller scale, then, you know, the medical device, sort of like bone integration or steer integration, sort of came from the small up, and we're sort of slowly meeting between these two chasms are coming together as we see different applications which have similar multi objective problems. And that's, you know, now it's moving its way up through the high performance automotive, aerospace, and then we'll go and then into, you know, sports equipment, and then it will slowly make its way into more like consumer facing objects, but for now, it's high performance, similar to what we've seen in in the in the engineering space, in additive manufacturing, where it has to be like something has to pay its way. So to to set up these systems, it takes time like you, if you're going to design a simple bracket, you don't need to computationally design that setup, because you can calculate load and figure it out pretty quickly to pay for that time of a, you know, basically someone who's half designer, half software engineer, to produce that workflow is an investment that you need to, you know, amortize. And so the product has to be of enough value or being used enough times to make that worthwhile.
Aaron Moncur:Does all computational design require some software skills? Or are there programs out there who have dumbed it down for those of us who aren't skilled in in writing software?
Duann Scott:Yeah, so there, there are companies who sort of help people build workflows which can then be reused by those who don't understand what's going on behind the scenes. Things like shape diver basically allows you to build a workflow in Grasshopper and then make a new expert in a browser, so people can then use that to modify a given geometry type to meet their requirements. So there are ways to access this without doing all the work yourself. And we also see in architecture mechanical engineering is that a larger firm will have a computational designer on staff, who will then build modules which other designers and engineers can then use, and they share that internally. We also see that at N top at anthropology, where I work for a period, you could also build these custom workflows which you could then share internally or externally, and then that would sort of give you a jump up, so you could then just bring in your your requirements, or some other data to then drive the geometry or simulation, and then run with that without knowing how to do everything from scratch, because there is a there is a learning curve.
Aaron Moncur:Okay, you've been involved a lot with standardization of file formats within the additive manufacturing space. You're laughing tell me why. What's going on,
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Duann Scott:You know, it's a file format. It's you young men don't dream of creating a
Aaron Moncur:file format. No one thinks about this, right? But it actually is really important to to the field,
Duann Scott:yeah, critical. It's painfully critical. So in my roles at both Autodesk and shake waste to a certain degree, but anthropology, I was working on partnerships between software and hardware companies, and each time we tried to do an integration, it took months of just making sure the plumbing would work and we could get data from the software to the machine and validate that everything worked. So. Yeah, and it was the duplication of effort and the small quirks of just decisions that were made 20 years ago, which in additive manufacturing is a long time ago, which still affect us today in a negative way. Just needed to be, needed to be overcome. So when I left NT a couple years ago, I was already sort of involved in the periphery of the three myth Consortium, and I sort of stepped in to help help, sort of with awareness adoption and to sort of get things to the next level on the political level, like I'm not doing anything technical, I'm not doing any coding. I'm not telling people how to structure these things, because that's not my area. And so I just helped to sort of connect things and get more growth of the adoption of the format. And now we also are now an ISO standard as of early this year. So now, technically, you know, you can you can rely we've been working this for 10 years, so it's got 10 years of testing, of validation by a third party, QA company. So we're not like and the consortium is from every major CAD company, software company and hardware company, who are usually bitter rivals. So you don't usually get the so Siemens at AUDIO DESK, sitting at a table trying to solve problems together. They're more likely to try and make things hard to switch between platforms. And this is a rare occasion where the technical people got to get together and without any sales or senior management people sort of blocking any sort of communication. And we have a data format which works to communicate more than just geometry in a very efficient manner.
Aaron Moncur:You mentioned this. This is a rare situation. Why do you think it is that technical professionals from these different entities, different companies agreed to get together and collaborate on this?
Duann Scott:Microsoft produced FOMO, so when there was the hype of 3d printing taking over the world, Microsoft looked at how it's going to be adopted, and saw the sto format and the other formats were under development, sort of in the space, and said, this will not do. This will not work. We need a robust format which is human readable and extensible so we can do everything that's required for additive manufacturing to bring it into mainstream. And once they sort of said they were working on this, then, you know, Microsoft has an operating system. Every CAD platform runs on Microsoft, pretty much, unless you're doing a server based on Linux. But, you know, every engineer has got a got a PC, not a Mac, because this is where the software is. So when they said we're working on this, all the cloud packages said, Okay, we'll do what we can to make sure that our systems work with this data format that you're proposing and putting investing in developing.
Aaron Moncur:So tell us a little bit about the three MF file format and how that has facilitated standardization across additive manufacturing.
Duann Scott:Yeah. Okay, so the core of the math file format is a mesh format, similar to what an STL is, except it's always watertight. It has no you know, you're not going to have an issue with the mesh not being solid, so you can't so you can always print it. It has units, so you know what size it is. So an STL format has no it's not a millimeter or an inch or a foot or Furlong or a yard, it's just some triangles, and the the it's more efficient because the vertices are counted once, instead of three times as as the STL is. So the base is just a way more efficient way of communicating the surface of an object. But then we have extensions for the production process, for materials, for slicing, for beam lattices, for lattice structures, for implicit for volume stacks, as in the bitmap stack for volumetric, free printing. So there's this way more data that you can encapsulate and communicate in the file format without increasing the size much depending on the flavor you're using. So companies as diverse as HP and bamboo labs use streamf as a container for their entire build information, not just the geometry, but all the parts within it, all the material specifications, all the machine specifications, and then any other metadata around the user's information, if it's a service bureau, is all embedded in this one format. So it's it's very robust and powerful.
Aaron Moncur:I just purchased a bamboo lab machine, and I'm very happy with it. It was cool, interesting to hear you talking about about that. So STL, you mentioned, right? I mean, that's ubiquitous. STL is like, it's everywhere. It seems like, in the world of Urdu printing, additive manufacturing, are there indications? That three MF is at some point gonna overtake STL file formats.
Duann Scott:I know human nature is is deeply flawed, so we never know what people are going to do, but I think the as the younger generation who are growing up with the Babu lab default to three MF now, because that's what they are growing up with it from school, yeah, and it's just this, the file is a third the size. So, you know, I know we have infinite hard drive space in theory, but we don't. And if you're sending stuff to a service bureau to get printed or stored or shared, if you're sending it as an STL files, it's three times the size, you know, you kind of swing into service to everybody involved, to have such a bloated format when that's just the basic thing, you know? So I think that as as the as the kids come up, and they'll default to three math, it'll slowly become this archaic thing, and someone out there will try and defend it as as still, as being superior, because that's what they grew up with. But there's no real reason to be using it anymore unless the software is also archaic. It doesn't support three MF,
Aaron Moncur:yeah, okay, all right, going back to computational design for a moment here, and thinking about traditional manufacturing processes like injection molding, you know, casting things like that is there, are there a lot of use cases? A lot of people listening to this podcast, they're, uh, they're using these very traditional manufacturing processes, injection molding and CNC and casting and whatnot. What? What are the use cases for those kinds of processes, with computational design, the product development expo or PDX is your chance to learn from subject matter experts, providing practical hands on training for dozens of different engineering topics, Gd and T advanced surface modeling, DFM, plating and finishing techniques, programming robots, adhesive, dispensing, prototyping, tips and tricks and lots more. PDX happens October, 21 and 22nd in Phoenix, Arizona. Learn more at PD Expo. Dot engineer, that's p, d, e, x, p, O, dot engineer,
Duann Scott:you you so the the ideal situation is you increase the performance of the part you've designed using this process and the manufacturing you optimize for the manufacturing Process simultaneously, so you could have rules in the design process about wall thicknesses. And like, if you're doing injection molded parts, you can optimize both the design of the part and design of the mold simultaneously. And so if you change sort of one requirement, like a material change or or a ejector pin change the design. Could modify with it, sort of automatically. And you could do a trade off. You could you could do like simulation of using machine learning would be the ideal way to do it, but you could do a simulation of sort of 100 different variations upon the same design and find the one which has the best trade off between manufacturing, time costs and performance simultaneously. So we're seeing that we had a company called from us who kind of manufacture a lot of the again, going to footwear. Major footwear companies shoes in in Asia. They're using computational design to both guide the design of the the shoes themselves, the performance, as well as the molds. So sort of using simulation and ladder structures for formal cooling of the mold to sort of decrease the cycle time, so it's not 3d printing is sort of leading the way in some areas, because it's such a gnarly problem and such an expensive process that to make it worthwhile, you need to sort of optimize for the design. And not everyone knows how to do that yet, so this is a way to sort of speed up that process. But we're seeing companies just like this, so using this for sheet bending and, yeah, and machining and milling. So it's not just attitude. For sure,
Aaron Moncur:you mentioned the phrase design for performance. What's the difference between design for performance and design for you know, insert your favorite traditional manufacturing process.
Duann Scott:Oh, this performance of the part after you've manufactured it. So you're optimizing for how it, how it how it performs in the world, whether it's, you know, a mechanical response, whether it's a like ensuring that it has an ecological, right sort of ecological footprint. So the performance can be any objective you have, the lifetime of the product in in use is when. I how I use that term and then process I think of as a manufacturing process, including any sort of post production inspection.
Aaron Moncur:Yeah, okay. I'm going to take a short break here share with everyone that the being an engineer podcast is brought to you by pipeline design and engineering, where we don't design pipelines, but we do help companies develop advanced manufacturing processes, automated machines and custom fixtures, complemented with product design and R D services, Learn more at Team pipeline.us. The podcast is also sponsored by the wave, an online platform of free tools, education and community for engineers. Learn more at the wave dot engineer, and we are privileged to be speaking with Dwan Scott today. So Dwan, let's talk a little bit about AI, which everyone is talking about. But how is AI starting to influence computational design, and where do you think it's going in the next few years?
Duann Scott:I think it's the other way around. I think of computational design as building the data platform for AI and machine learning. So AI and machine learning is only as good as the data you're feeding into it if you feed it images of rabbits and and like game geometry or poorly constructed CAD that's in a database from people who have abandoned their files because they don't care about them. The the results you'll get from training your AI or text the CAD system will be that garbage. So you're not going to get anything good out of unstructured, public data, from what I've seen so far, when you're building a computational design workflow, or even just like you have the mindset of how you're building a computational design workflow, you're bringing data together about the performance of A part, the materials, the manufacturing process, costs, like recycling, like all this other information about what you what your design intent is, and what your manufacturing intent is, and then you're using that to make a decision. So once you have all that data sort of that you're using to analyze, you can then use that to train machine learning to then accelerate decision making next time, for something that's similar, not for a generalized you can't use the data you've used to design the bracket for air conditioner to then design your jet engine. That is not how things work. So if you're working in a particular field, and you've got similar problems, then the data you gather during the design process, if done well, can then be used in the future to accelerate the design process, engineering process, not to replace it. You're basically cutting down the cycle time for simulation or for problem solving or for getting opinions from people, because you sort of had those opinions baked into your algorithms, which are then used to, you know, you can then use AI slash machine learning to Give you a down selection of results within the credit curve of results you've done previously. So you can't use it to design something outside of your known that you can use it to find a valid solution which meets your requirements inside of it.
Aaron Moncur:Let's go back to cdfam for just a moment here. I'm curious to hear a little more about how you started this, because it's, it's grown over the years, and I think, hopefully I'm not misremembering this, but I think you remember telling me that it's now in the US and European countries. Is that accurate?
Duann Scott:Yeah, I currently run it twice a year, once in New York and then once in a European city. So it was in Amsterdam just a month ago now, and then last year it was in New York and Berlin, and next year it's going to be in Barcelona, New York and Tokyo. So I'm going to run one in Asia as well, because people having trouble traveling that far to go to events in Europe, on us, yeah. So it started because it was the event I wanted to go to, yeah. So I've been, you know, in the design and 3d printing space for a fair amount of time. And the events that I went to were very machine, material and process focused. There wasn't a lot about design and software. And so, you know, I would present it every conference, typically, and I would be there on Thursday afternoon, when everyone else is the airport talking my talk. And then, you know, there'd be. Be a few excited people, and then everyone else have left. So I just want to bring people together who are excited about design as I am, so we can sort of share ideas and learn from each other in a very non salesy, pitchy way, but a very like just pure knowledge sharing and networking. Yeah, no,
Aaron Moncur:this is very interesting to me, because I am in the middle of developing PDX, the product development Expo, which is, is happening for the first well, technically, for the second time, but in the the first real way this October, 21 and 22nd out here in Phoenix. And I had the same idea as you, which is, this is the event that I would like to go to. This is the event that I think would be fun, and it doesn't exist, so let's build it. Yeah, it's been a ton of work, and I've learned a lot, but very satisfying work. I imagine your experience has been similar. I'm curious what were, what were a few of the biggest challenges that you had to overcome in the beginning.
Duann Scott:The beginning was not knowing whether there was enough people who would be willing to commit their time to come and present, and then whether there's enough people to come and fill a room to make it worthwhile for those people to present to. And I was I being in the space for a while, and so I've had a decent neck with the people I could call on and ask if they're interested in so we, I got a good program to start off with, with some really exciting speakers. And it was, you know, it was everything that I hoped it would be in, that it was informative and informal and fun and inspiring, and you know, both down to very technical things at material level, up to conceptual like there were people who were using AI and machine learning to develop and analyze and sort of rank material performance down at The literally molecular level. And then there was also someone who was working for Yeezy, using large scale robots to screen print mycelium for shoes in a very experimental way, you know, and
Aaron Moncur:mycelium for shoes, I hear about mycelium for mushrooms, I don't context, yeah.
Duann Scott:So this researcher, she was from Australia. She moved to LA to work with Yeezy now Adidas, and she had done a PhD by training a robot to be responsive to the growth of mycelium and feed it to make it grow in a given form. And then, gosh, yeah, that's crazy, yeah, as well stuff. And that led her to be poached by Adidas to think about how they could rethink footwear and packaging using natural processes, so emergent forms, so these are on the same stage. So that's what I was looking for. This broad ideas shared, and then how people make the connections between them is what's interesting.
Aaron Moncur:That's wild. I'm no expert, but my understanding of Mycelium is it's the kind of thread, like interwoven lattice structure underneath the ground that eventually forms the, you know, the fruit mushroom that that we see above ground. So you're saying training a robot to feed that mycelium in a way that it grows into a very specific, intentional shape, like a shoe sole or something like that.
Duann Scott:She was doing it into an emergent shape. So it was like, it was like an interaction. So this robot took like, two months to grow this mycelium. So it was slowly feeding. It had a sensor and seeing when an area was dry, would drop some nutrients and water in that area, so it would continue to grow. It wasn't making it a performant footwear at that stage, but the concept of, like, how we can evolve forms, you know, and sort of what's interesting is sort of the the design process and the manufacturing process, in a very abstract way, completely linked, like they're inseparable. So if we think about the design manufacturing process, there's this inseparable flow between of data going through it. This is kind of like a very organic, abstract, kind of almost art approach to this, which can then change the way we think about how we design an injection molded part.
Aaron Moncur:Perhaps that's fascinating. Never. I had never heard about that. Okay, where can people go and learn more about
Duann Scott:CD fam, cdfam.com CD as I said, the URL was $1 it's easy to get to cdfam.com the next event will be in October 29 to 30th. So just before Halloween in New York. Halloween in New York's always fun. We've got an amazing lineup, again, of people from, you know, the research from MIT using AI and machine learning to develop materials, again, because I think this is really important, up into a number of footwear, people from New Balance and Puma. And then, because we have footwear that we have, going to have architecture. So we have people using, you. Machine learning and sort of other interfaces for for understanding the data that's driving decision making in the building, in the built environment. Yeah. So we've got a lot of interesting there's a lot, there's more and more machine learning coming into the event as the field evolves and things become more real.
Aaron Moncur:That's terrific. Was there ever a moment over the past, you know, several years as as your symposium has grown that I don't know you overheard a conversation, or maybe a friend forwarded you something, and you realized, wow, the CD fam community is really having an impact in the industry. This is exactly what I hoped would happen.
Duann Scott:It's only been going since 2023 so I'm only a couple of years in now, but I constantly hear of partnerships and adoption of new technology because they found out about it at cdfam, which is like the ideal situation is, it's about bringing people together, and it's, I won't say this is entirely true, but the outcomes, like the physical outcomes, what are made with it, aren't as interesting to me as the way we approach things and how we work together. And so that's what I'm really encouraging. Because, you know, I can't be an expert in every application of everything that uses every software like that's that's not gonna, that's not gonna. I can learn a lot in these in the process of building these events, but what I sort of can find is how to get people to work together from from broad fields, to solve when they have an abstracted version of similar problems. And when I see you know someone from the medical device space being called in to work with someone in the aerospace industry for for a given like a filter they're thinking of like so if someone did a presentation on on how a a osteo integration may happen, and then they're using that same idea for filtering chemicals to help with a chemical reaction. That's cool. That's what's interesting, like when it's abstracted out.
Aaron Moncur:Yeah, yeah. Do you have any pro tips for for how you have seen or facilitated disparate groups working together, right? You might mention like medical devices and different industries and seeing them all come together at cdfam and work together and collaborate. Any pro tips for others of us out there who might be trying to bring dissimilar industries together to collaborate as well. I think, looking for the
Duann Scott:similar problems, which is multi in my world, it's like multi objective, multi stakeholder problems, whether a stakeholder might be a surgeon, manufacturing engineer and the client who's getting an implant, or a developer, a council, an architect and a client for architecture is sort of like these multiple problems. Everyone's got very strong opinions about what they need, and often they compete against each other. So finding, you know, gnarly problems like that is where I'm finding a sweet spot in, in finding people who then have the same sort of multi objective problems at different scales, or a different output or a different input.
Aaron Moncur:Yeah, terrific. All right. Well, Dwan, where? Where can people get in touch with you?
Duann Scott:I think there's a contact form on cdfam.com, that's probably the best way to go. Okay, I'm on, I'm on, I'm on LinkedIn as well, if, uh, connect personally with me, and I usually answer messages.
Aaron Moncur:All right. Well, Dwan, thanks so much for your time today. Is there anything else that that you want to bring up about CD fam or cool file formats, or anything else that you think would be useful or listening to the listeners out there that we haven't touched on yet.
Duann Scott:If you're transferring or storing data for D printing for any application, you're kind of doing a disservice for yourself. If you're not using three MF, it's free, it's open source, and it's extensible. No matter how complex your problem is and how niche you can use it to solve the problem for you and your customers, your clients, or whatever. If you need to help applying it, you can ping me, and I can get someone who's technical to help you apply it. If you want to learn about more design approaches to design, check out cdfam. All of the recordings and the presentations that are legally possible are free online, on YouTube and as a podcast, not as like questions and answers like you're not doing, but just as a recording. So all the. Information is free where possible, yeah, and have fun. There's exciting new tools and exciting purchase to design which are opening up, and it changes the way we can solve problems.
Aaron Moncur:Fantastic. Dwan, thank you so much for creating CD fam and building that community, and also for sharing some of your precious time with us today on the podcast.
Duann Scott:Thank you. It's been fun.
Aaron Moncur:I'm Aaron Moncur, founder of pipeline design and engineering. If you liked what you heard today, please share the episode to learn how your team can leverage our team's expertise developing advanced manufacturing processes, automated machines and custom fixtures, complemented with product design and R D services. Visit us at Team pipeline.us. To join a vibrant community of engineers online. Visit the wave. Dot, engineer, thank you for listening. You.
