3D Printing’s Latent Flaw & the Data-Driven Technology That’s Overcoming It

Like most Data and technology professionals, I thought I had my head wrapped around the concept of 3D printing quite well. The basics? Use some sort of CAD software to design a part, then print the part. Voila! You’ve more or less developed a functional prototype or production part in record fast time, with a shiny new technology. You are part of the additive manufacturing (AM) movement, and welcome to the vanguard of the 4th Industrial Revolution. Right? Well, not exactly. 

I was pretty blindsided to learn that there’s an entire additional step that nobody really likes to talk about; post-printing. 

So - what’s post-printing in AM? 

And - why is it significant to the future of Industry 4.0? 

And perhaps most personal to me, why did I recently join the Board of Directors of the world’s first company to automate this process with data connectivity, PostProcess Technologies? 

What is post-printing?

When we say “post-printing”, the term encompasses everything that has to happen to an additively manufactured part once it’s been taken off of the 3D printer. Famously, my PostProcess Technologies colleagues refer to this step as “the dirty little secret of additive manufacturing”. Depending on how it was designed and which print technology was used, a 3D-printed part may have to undergo support removal, resin removal, surface finishing, or a combination of these, in order to function as intended. Almost 90% of all printed parts require post-print removal of some kind, per this industry survey report conducted in 2020, while ~70% require some sort of surface finishing. The issue is, the methods to finish these parts are more or less stuck in the 20th century - think sand/water blasters, submersion tanks, or, in some cases, even laborious hand sanding. 

Needless to say, this bit technologically pales in comparison to the highly digitized “design” and “print” steps of AM. These outdated processes may not seem cumbersome to a hobbyist 3D printer, but for manufacturers, post-printing may be the barrier that’s actively preventing them from scaling to production volume printing. 

Throwing more engineers and technicians at the post-printing problem might seem like the obvious solution to make the post-print processes less of a burden, but highly-trained employees could be making better use of their expensive time. 

The best-case scenario is an automated systemization of the post-print process that can leverage software and Data to finish parts quickly, with minimal technician time or human error. It’s the implementation of the classic saying, “work smarter, not harder.” In essence, it’s what Big Data and digitization is all about. 

A 21st Century Solution

PostProcess Technologies is a fast-growing company that, in an effort to tame the chaos and waste associated with additives “dirty little secret”, introduced the world’s first automated and intelligent post-printing solution to the world back in 2014. Instead of approaching additive as a process with three siloed steps (design, print, post-print), they developed an appliance approach that leverages data and analytics to optimize and automate the work needed to post-process a printed part and make it ready for use. 

No matter how extensive or minuscule your knowledge of AM is, it’s clear that virtually any organization implementing additives could benefit from automated post-printing. It’s this bigger picture that motivated me to join the board of PostProcess Technologies. 

Systemizing Data - A Recurring Theme

Simply put, systemizing with Data is a mission that seems to find me no matter what field I’m working in - from observing stars and galaxies back in my Caltech/JPL days to laying the groundwork of modern digital advertising at Yahoo! to applying data science and BigData to Barclays financial operations, and now working with large enterprises to help them turn data into valuable business-driving assets at Open Insights.

During my time at NASA/JPL, I learned the value in leveraging Data and machine learning in working with astronomers to help them tell apart stars from galaxies for the majority of objects in a sky survey — faint (i.e. far) objects in the universe. This work showed that machine learning can solve very difficult statistical and image analysis problems quickly and accurately. This enabled a truly revolutionary approach to cataloging over 2 billion faint objects in the northern sky and enabled much new science.

Once my second start-up was acquired by Yahoo!, I spent time as the world’s first Chief Data Officer connecting Data to advertising, which effectively spawned the birth of behavioral targeting and a new generation of data-driven marketing. These predictive modeling technologies coupled with BigData enabled Yahoo! to extract billions of extra dollars from ad impressions that were sold at huge discounts without targeting.

As someone who has been privileged to participate in data-enabled transformations of several fields (autos, manufacturing, telecom, finance), it’s exciting for me to see PostProcess stepping up to connect additive’s digital thread. Leveraging data, AI, and chemical appliances to address a difficult and important problem.

A Few Words on the “Dirty Little Secret”

The term used by my PPT colleagues does not only refer to the unknown pains in dealing with 3D printer outputs, but also actually reflects a serious environmental issue. Optimizing the yield of AM outputs not only minimizes environmental impact, but actually the chemicals and abrasives used in post processing — the consumables — can be challenging to manage in an environmentally responsible way. Disposal of used chemicals is an expensive and burdensome task. By leveraging data and AI, PPT optimizes and minimizes the use of these consumables along with increasing yield (minimizing number of redos). This creates a more sustainable and more economically feasible solution to the issues at scale. Not to mention minimizing the health concerns of exposing humans to harmful chemicals and fumes. The appliance approach keeps it contained in a “box” that minimizes effort and consumption. 

Final Words

Once data is captured and refined, I’m confident that the Data made accessible by PostProcess will act as a turbocharger for AM, and Industry 4.0 as a whole. It is yet another virtuous cycle enabled by leveraging data, software and AI. The post-printing space is fragmented as is, but this sort of Data will help beyond just streamlining efficiencies. The more we aggregate across many trials and AM outputs, the better we will be able to mitigate waste and make more sustainable manufacturing decisions. I’m thrilled to be on the board of a software company so heavily invested not only in their own growth, but in the enablement of an entire industry of the future.