Artificial Intelligence’s Role in Additive Manufacturing Continues its Upward Mobility

The two have us closing in on a potentially massive tech and business revolution

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10/07/2022

Kris Alvarez

 

 

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The world of artificial intelligence (AI) has seen some promising developments in recent months—and no, I’m not referring to Synchron successfully implanting its brain-computing interface (BCI) chip into its first US patient over the summer (you’ll get ‘em next time, Elon). Things like machine learning algorithms being used in climate modeling to the launches of text-to-image models like DALL-E 2 and Stable Diffusion are really showcasing the rate at which AI implementation is expanding. Much like the buzz around AI, there’s been continued chatter around additive manufacturing (AM)—another disruptive tech with a global market value forecast to reach a whopping $49.48 billion by 2027.

As the saying goes, “it takes two to tango,” and AI and AM have long been doing a beautiful dance that everyone ought to start marveling at—one that is sure to change the ways businesses (and even consumers) operate across nearly every industry imaginable.

 

 

Something New Derived From Something Not-So New
AI isn’t anything novel by any stretch of the imagination. In fact, earlier this year, our very own Greg Cholmondeley touched on AI’s formative years, how far it’s come since its inception, and its foray into the production print space. But AM isn’t any newer to the conversation either. From the 1980s, AM technologies (i.e., 3D printing) evolved rapidly beginning with the introduction of expert systems and the creation of the first 3D printer, so it’s no surprise that both continue to garner so much attention in tandem—even today. What’s striking, though, is the sheer vastness of their application, the ways in which its being multipurposed, and the potential they have to eventually change the course of traditional industrial manufacturing processes.

Let’s take process surveillance and control for starters, one of the many applications of machine learning. By incorporating algorithms and AI, manufacturers can easily and effectively detect part defects to minimize print failures, not to mention reduce the time spent troubleshooting said failures—all while keeping materials costs low. Moreover, this level of algorithmic data science presents the opportunity for seamless design process refinements over time. It helps facilitate a closed-loop system of materials engineering for the manufacturer and allows greater control in the improvement as well as adaptability of the 3D objects being created.

 

 

But Is it Sustainable…?
This is where waste production and the assumed environmental impacts of AM come to rear their ugly head. But, by nature, additive manufacturing revolves around just that: the addition of raw materials by layering through computer-aided design (CAD) software rather than stripping away or extracting various materials as with traditional manufacturing. As such, additive manufacturing aims to alleviate the stresses brought on by traditional manufacturing’s waste production, which have plagued a host of industries from aerospace and automotive. Not only is AM addressing the issues surrounding physical waste, but energy consumption and productivity, as well. AM is said to reduce energy use by 25% and could cut materials cost and waste production by up to 90%.

 

So, Why Aren’t Things Moving Any Faster?
It all sounds pretty sweet, right? Well, that’s because it is. But, while there’s obviously a very sound marriage between AM and AI, there’re always caveats and known unknowns when it comes to innovation of this caliber. One of the challenges stems from the level of computing hardware needed to effectively run deep learning and machine learning algorithms for AM (GPU processing power specifically), which some would argue could combat AM’s reductions in energy use due to how power-hungry they can be. Another potential pitfall comes from having a plethora of experimental learning data from researchers across the globe and having no place to competently store it. It’s paramount that the AM community work in concert to ensure that data is represented accurately and concisely for additive manufacturers to better execute their processes. This ensures future AI models have an easier path forward, too.

 

Keypoint Intelligence Opinion
While still in its infancy, there is (and will continue to be) a lot to be excited about when it comes to AI and its ability to push AM even further than where it is now. We’ve already witnessed varying degrees of AI being incorporated into so many different business areas, including office printing. Here at Keypoint Intelligence, we’ve already dipped our feet into these waters after having tested the FormLabs Form 3+ several months back. We’re seeing companies like Hyperganic dedicate years of research to generate their own design models for producing 3D objects. I mean, even houses are being 3D printed now.


Like anything else, we’ve still got quite a few kinks to work out before we can fully realize global change through AM with AI in the driver’s seat, but we’re eager to see how things take shape in the future.

 

For more information on 3D print, head to The Key Point Blog and The Key Point Podcast. Please contact pete.emory@keypointintelligence.com if you have any questions about additive manufacturing testing at Keypoint Intelligence or want to pitch us a product to test.