“Down the Road” is Nearer than You Think: Why Nexa3D Is Bullish on AI for 3D Printing

“My prediction is that by 2030, there are going to be two kinds of AM companies,” Reichental said. “Those who fully embraced and benefited from AI, and those who are out of business. And I say that about this industry in particular, simply because it’s the industry that I’m in, but I think you can say it about any industry.”

“AI was conceived in a conference at Dartmouth College in 1956,” Reichental noted, referring to the Dartmouth Summer Research Project on Artificial Intelligence, an event viewed as marking the origins of AI as a field of research. “AI is my age. In the beginning, it was just a concept. I remember doing AI exercises in workshops 10 years ago with cards, which illustrates very simply how the concept works — but without ubiquitous connectivity, without infinite computing power in the cloud. When we have our discussions internally now, as an enterprise, it’s almost like, forget about additive, let’s talk about digital manufacturing.”

“Essentium released two material formulations last year that I was particularly proud of our team for brining to market. One is a material called Duratem, where we basically took the chemistry of ULTEM, which is a polyetherimide, and we use siloxane treatments to modify the chemistry. Basically, we took the stiff parts of the polymer chain and we broke them up with some flexible bits that creates an ULTEM, aerospace-worthy material that is now ductile and has some resilience to it, but still survives the flame, smoke, and toxicity requirements for airworthy parts.

“But it took us like 18 months to develop that material. We went through a whole bunch of custom formulations and all this deep chemistry and material science R&D to make it happen. With the use of AI, we could’ve gotten there so much more quickly. And then we could’ve immediately started focusing on the implementation of the material to save time and money for aircraft parts.”

“Altitude is a polycarbonate, so like the same material as hard hats and safety glasses, but it’s got a UV stabilizer, it’s got a thermal stabilizer, and it has anti-microbial properties—all of these packages that we put into the material to make it useful at very low temperatures and useful for applications like drones and UAVs. The goal was to make a material that’s both low-cost and easy to use. But again, the use of AI could’ve greatly accelerated our path to optimizing the ratios between all those different properties.”

“In my two decades in the industry, the slow adoption or the friction or the blockage point that stands between what we as OEMs build, and how much of it our customers can successfully onboard and implement, has to do with expert, labor-intensive work in the pre-processing phase,” said Reichental. “It’s a very steep learning curve. We know of hundreds of companies over time that wanted to adopt 3D printing, brought it in-house, and within six months or a year, they push it to the side. Why? Not because the printers didn’t work, but because they underestimated the level of complexity and expertise required to adopt it successfully at scale. AI takes most of those obstacles away just in the pre-processing phase.

“Then you begin to look at the printing phase itself, and with sensors, you can collect real-time data, process it, and make your systems more and more intelligent. We’ve been printing for decades, but the printers don’t know what they print. So if, all of a sudden, the printers gain that capability — if they can learn — yields improve. The ratio of labor intensity to parts printed decreases. The learning curve shrinks. All of that delivers tangible value. We’ve been talking about wanting to democratize access for years, but we really haven’t done much to remove the biggest barriers to adoption. AI can enable you to do that, which is why we think about it as a wingman.”