Why is 3D Printing Developing at a Snail’s Pace?

In 2009, I gave a presentation on the state of 3D printing and, out of 18 slides, I’d only have to change one for that talk to be accurate today. Imagine how much computers have changed in that time span,…

In 2009, I gave a presentation on the state of 3D printing and, out of 18 slides, I’d only have to change one for that talk to be accurate today. Imagine how much computers have changed in that time span, how fast the games or the mobile phone industry has evolved over the same period.

In 2009, popular cell phones included the iPhone 3GS and the Blackberry Storm, which had “EV-DO Rev. A and UMTS/HSDPA support. Other highlights include GPS and a 3.2-megapixel camera.” 2009 was also when Minecraft and Bitcoin were introduced and Halo 3, The Sims 3 and Wii Fit were popular games. While we danced to Lady Gaga’s “Just Dance” and “Tik Tok” was a song, not an app, our 3D printing technology was pretty much the same as it is now. Since then, powder bed fusion got a bit more productive and parts were whiter, UV degradation was reduced a bit for components made with vat polymerization, and fused deposition modeling (FDM) printers became a lot cheaper. There are more materials now and many more vendors, but fundamentally little has changed. So, why is this? Why is progress in 3D printing glacially slow? Factors that have retarded our growth: We’ve underutilized software to push our industry forward. In many areas, software has increased productivity, as well as allowed people to communicate directly and work together. In 3D printing, software has made it easier to send, receive, slice, and nest files. But, crucially for most print jobs, there is still a necessary human element. Even in highly automated processes, a human needs to check or bring a massive amount of experience to bear on the optimization of the builds. One-click printing is still a dream, but we cannot automate authoring, file checking, and nesting completely at the moment. The only exception are some digital supply chains for the dental market, but even for Invisalign and hearing aids, a person is in the loop. There was little production going on until recently. There was not enough manufacturing at enough sites to justify a market in post-processing equipment. This meant that everyone had to cobble together their own supply chains and post-processing techniques and wasn’t able to buy standardized labor saving devices. Manual labor is a large component of costs. Manual labor is still a third of part costs because there hasn’t really been much investment and revenue in post-processing until recently. Now, companies have the wherewithal to completely automate post-processing. We’re still paying too much for materials. We routinely still pay $65 or $90 for a kilo of polyamide, while elsewhere people would pay $9 a kilo for the same stuff. We sometimes pay $120 or $30 for a kilo of ABS, where others would pay $6. We pay $20 for a kilo of PLA instead of $4. Many photopolymers are more expensive than titanium powder, which in itself is too expensive. This retards growth, applications, industrialization and new investment. Everyone was being held captive by their own IP. Everyone only developed technology within their own IP, which meant that technologies were often only supported by few companies. Most companies focused on low-volume, high-margin. Materials companies focused on high margins at comparatively low volumes, while services focused on making high-margin parts at low volumes. This meant that there was, until recently, no real impetus for change. Machines were Lab and not Fab. Machines were made for universities, which meant that they were designed for research and development. We had lots of different options and settings, but machines were not made for manufacturing. The Successful manufacturing cases were not copied. A strange thing really is that, until recently, many successful manufacturing cases were done by one or maybe two companies. The fantastically successful Invisalign industrialized vat polymerization for dental molds by itself and, surprisingly, was not copied until recently. So, weirdly, there are few companies that have copied it to make mouth guards, headphones, or other silicone mold applications that would benefit from unique, 3D printed molds. Also, only recently have well-capitalized companies gone after Invisalign’s business itself. Avio Aero worked alone in 3D printing turbine blades for years and BEGO fully automated the file handling and part automation for metal printing of bridges and crowns years before anyone really competed with them. Investment in 3D printing was low. For the first 25 years or so, investment in 3D printing, especially in fundamentally improving or replacing existing 3D printing technologies, was low. Apart from a lot of work at Fraunhofer, Oak Ridge National Laboratory, Sandia, and a few others, few did the heavy lifting. Only after 2011 did we get serious stock interest. After, there was some serious VC interest, but only in a few companies and only really until this year did we see serious IPO interest. Only now are we getting many billions of funding. Why is it difficult for 3D printing to become better? It is hard to make a 3D printer. A powder bed 3D printer is a combination of physics, optics, lasers, material science, mechanical engineering, motion control, and software. All of these disciplines must come together in order to make a good 3D printer. Well-balanced teams holistically improving devices on all fronts without reinforcing detrimental feedback loops is a difficult thing to do, especially if complexity increases. It’s harder to make a larger or more productive printer. As you grow larger in part size or build volume, accuracy is more difficult to maintain. The strength and stiffness of all chassis parts must be improved. Stability becomes more important as do vibration, temperature control, and feedback loops. As complexity increases, part counts often increase, while at the same time higher precision and strength are needed. This means that to go from a working machine to one that has a significantly higher yield or is twice as big is very difficult. Quality assurance (QA) for 3D printers is also complex, as it requires the checking of many different and many different types of assemblies and systems. We didn’t have the data or practices. With so few manufacturing cases taking place and even fewer shared publicly, data was limited. Industry best practices were hidden in tribes at GE, Phonak, BEGO or at service bureaus. So, best practices didn’t become commonplace. Also, for people outside this very limited group, it wasn’t apparent what the actual day-to-day challenges were in 3D printing for manufacturing and what was needed to improve it. Innovators, investors, and researchers were often not clued in to what the actual yield, error rates, problems, wastefulness and inefficiency was. Insufficient actual resources were therefore attracted specifically to solving real problems. We’re making machine tools. With all of our newfangled complexity, we’re still making machine tools. We’re manufacturing equipment that you should be able to run for a decade, pretty much 24/7, while expecting them to always give you the same results. This is not a gadget or a simple device that is easy to assemble and then works forever with no moving parts, like a computer router. It’s not a PC or mobile phone, whose QA testing you can do simply either. We’re talking about a complex device, with a lot of moving parts, with a lot of different types of systems that has to work accurately, repeatably, and reliably for years. This is a very difficult challenge. Failed dual-color Benchy [Source: 3Dbenchy.com]We make parts in a completely new way, each and every time. Every single component produced on a printer can have different characteristics or properties depending on the orientation, location on the build platform, and just what tool path was used to build it in a particular way. Output from 3D printers is still highly variable run-to-run or across the lifespan of the part. We’re not taking a block of a known tested metal and cutting it the same way every time. We’re taking a tube of toothpaste and building up a part that hardens differently with different tool paths and with varying surrounding temperatures, shapes, and airflow. So, we’re uniquely making unique things, even if all of them would seem to be the same at first glance. A print bed full of the same objects are actually each and every one different, made differently. Famously, complexity is free in 3D printing. For a long time I’ve thought that this was a problem. Only now are we really tackling the bugbears, fundamental issues and real problems that are holding back 3D printing. We’re an industry that wants to make highly productive machine tools and we should approach the challenge with real humility. We should be in awe of the industries that we are to compete against. 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