Enclosures are the underappreciated CNC machined components for aerospace and defense. When those of us in manufacturing think of the machined parts most characteristic of aircraft, we might think of contoured structural components or engine components made of high-temperature alloys. Enclosures are the sophisticated rugged housings protecting electronics, generally machined from aluminum, and they are becoming all the more important with the rise of more capable aerial weapon and reconnaissance systems that fly unmanned.

Advance CNC Machining, which focuses on precision four- and five-axis machining center work, has become an enclosure expert. For various customers (not all in aerospace, some in marine applications), Advance CNC has produced more than 100 enclosure part numbers. What these lightweight precision parts have in common are critical sealing requirements; cooling details ensuring the temperature stability of the electronics within; and elaborate geometry conforming to the electronics assemblies.

From a distance the enclosure may look like a metal box. It is that, albeit a metal box with a lot of critical machined detail.

I filmed this video at Advance CNC to capture some of what I have learned about these parts. Here are some of the machining challenges and considerations of producing aerial system electronics enclosures:

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The purpose of quality standards is productivity.

That statement seems false. The purpose of quality standards ought to be quality, and holding everyone to the same high level. Standards should be about excellence.

But no. In a capable manufacturing culture staffed by talented, skilled, conscientious team members, everyone is excellent. Or, at least, everyone wants this and aims for it.

The problem is that excellence is subjective. Meanwhile, cost, time, and customer acceptance are the measurable factors by which success is defined. The impulse toward excellence has to be translated into the realm of measurable deliverables, and this is what quality standards do.

The peril of absent quality standards is not poor quality. The peril of absent quality standards is, in a way, too much quality—meaning a process slowed down by misunderstanding and argument among well-intentioned people who see quality in different ways.

I encountered a great story of this. Allied Cycle Works had an inefficient process when its bike painting aimed for individual excellence.

Painting is perhaps the ultimate subjective manufacturing operation. As Allied director of product Sam Pickman explained to me in our conversation, every paint job is imperfect. There is a vast terrain for difference in what level of imperfection to accept.

What defines a great paint job? Everyone has a point of view.

However, once Allied learned how to define paint quality according to an objective standard, arguments ended and productivity rose.

Pickman (above right) describes how the Allied team members created this objective standard for paint. The useful, measurable metric the team found is time spent painting, which differs for bike surfaces with different levels of visibility.

Meanwhile, Pickman also speaks to the larger question: How does a team define standards and get everyone to follow them? This question applies to every organization trying to work faster and more predictably by getting everyone on the same page. Pickman involved every painting stakeholder in the conversation not because of a spirit of inclusion, but because he needed everyone to understand how the quality standard was reached and what its metrics mean.

My conversation with Pickman about how to define and implement a quality standard is the topic of the latest episode of The Z Axis. Watch it here:

This is our second episode with Allied. Check out our ealier episode all about this bike maker’s experience with U.S. manufacturing, and finding the right part of the bike to make in America.

The Z Axis is sponsored by Workforce Factor, a customizable onboarding system for industrial employees. Among other things, it provides a way to quickly bring new hires up to speed on the special, internal quality standards unique to a particular company. Learn more at the link below.

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But imagine CT scanning as a daily industrial metrology tool validating ongoing production. Such a tool would do more for measurement than a coordinate measuring machine (CMM) can do. CT scanning can deliver fine-detail measurement of the exterior geometry of the part, like a CMM, but then also adds fine measurement of any detail hidden inside.

Speed has been the limitation on this scenario. CT scanning moves too slowly to keep up with production. Except: Researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility have succeeded at making CT scanning faster.

As you might have expected, AI is the key. Part of why CT scanning is slow is the amount of sampling needed. Measuring many different angular sections provides enough data to overcome inherent noise. But in place of all this redundant measurement, deep learning via the Oak Ridge technology, called Simurgh, can now deduce accurate measurements past the noise with far less data.

Parameter development in additive manufacturing was the first beneficiary. Part of what makes AM parameter sets effective is their ability to print fully dense metal without internal pores. Using Simurgh, an array of laser powder bed fusion test specimens in a new metal, all made with different parameters, can be CT scanned rapidly to now determine in a relatively short lead time which parameter set is best.

But scale production, whether additive parts, machined parts, or castings, is the application where fast CT might deliver even greater value.

In the latest episode of Manufacturing Unlimited, I talk with ORNL MDF researcher Obaid Rahman about the promise of fast CT and whether we might see CT scanning begin to fill in for CMMs.

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Manufacturing Unlimited is produced by ASTM International and sponsored by the International Conference on Advanced Manufacturing (ICAM), where Rahman will present on this technology. Learn more at the button below.

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For the final installment in the series of videos I have been filming with Nate Stevens of Lifestyle Additive, we talk about applying selective laser sintering (SLS) additive manufacturing for production. SLS is the AM process that inherently wants to do production, for reasons Nate lays out in the video. They relate to the special characteristics of SLS materials (comparable to polymers already used in established production processes) and the lack of support structures allowing for large quantities per build through 3D nesting.

But the cost is not in the printing. Not all of it, that is, and not even most of it. When it comes to production, the way to understand additive manufacturing is not that it is contained entirely in the 3D printer, but that it consists of all the operations in the sequence following it as well. A streamlined, unattended 3D printing cycle could be followed by a time- and labor-intensive set of steps for powder removal, surface finishing or even inspection. To design a part for production SLS is therefore to design it to speed through the steps that follow SLS, and this video considers what that means.

Example: Adding a hole to the design of one part simply to let it hang from a hook sped the part’s setup time for vapor smoothing. Engineering designers involved in large-quantity production immediately recognize the power and importance of design tweaks such as this. SLS is the AM process capable of such large quantities that the “seconds matter” mindset of production enters into succeeding with this process as well.

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Human choices have magnified impact when automated or streamlined systems account for more.

Plus, organizational culture plays a larger role than ever in how manufacturing continues to advance.

The latest episode of The Z Axis considers an example. I talk with Patrick Curry, one of the family owners of Fullerton Tool, a maker of cutting tools for CNC machining, about the way the company’s V-Mac end mill was developed. Culture and human choice were paramount in the tool that actually came to market. From this example, consider how human factors are paramount in all the manufacturing technology advances we either do or do not see.

Curry and I filmed this conversation on the production floor where Fullerton grinds its cutting tools. The facility is so clean it works beautifully as a set.

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Not incidentally, this story is also a picture of the impact of the International Manufacturing Technology Show (IMTS), and the effect this show has on driving technology. The Fullerton team scrapped product and redesigned a tool that was already working to have it available in time for IMTS.

The Z Axis is my show about new directions in manufacturing, sponsored by Workforce Factor, a new platform for onboarding industrial employees that is tailored to the culture and tribal knowledge of individual workplaces.

If you are subscribed to this newsletter, then you are already a Z Axis subscriber, because I will include a post here about every new episode. But if you would like to get video content in its native platform (or if you would simply like to offer my work a vote of support!), you can also subscribe on YouTube.

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Two major, overlapping reasons:

1. Additive manufacturing does significantly more than CNC machining. LPBF produces not just the part geometry, but instead the part geometry plus the microstructural properties of the material of which the part is made.

2. GRR builds confidence from repetitive measurement. Measuring the material properties produced through LPBF, among other metal additive processes, generally involves coupons produced for destructive testing. Destruction rules out repetition, so there is less opportunity to build a legacy of data.

Still, the data record is being built, however slowly. And LPBF has now been producing long enough that the volume of data available to some users is large indeed, perhaps transformatively so.

In short: Get enough data, and statistical confidence along the lines of GRR becomes possible. This is the new age of metal additive manufacturing we are now entering.

Haley Cook and I discuss this in the latest episode of Manufacturing Unlimited, produced by the ASTM Additive Manufacturing Center of Excellence. Cook (seen above) is Senior Director of Operations for Metal Additive Manufacturing with ADDMAN, which is one of the LPBF users leading in terms of scale, history, and access to data. ADDMAN runs more than 50 LPBF machines across three sites in the U.S., and it has sizable destructive testing material data histories even for more obscure additive manufacturing metals such as C103.

I met with Cook at ADDMAN’s North Carolina facility (the largest of its LPBF sites), where she described what she and others within ADDMAN are coming to understand about the power of the volume of data now available for LPBF process characterization.

There is a threshold we are about to cross with additive manufacturing, she says. Statistical confidence will reduce our need to rely on 3D printed test coupons and destructive testing (such as tensile specimen testing, as illustrated in the images above). This in turn will expand the opportunities for additive, because destructive testing has been a tax on additive so far. Reduce the role and requirement for destructive testing, and additive manufacturing for metal part production will become cheaper and easier to use.

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For even more, Haley Cook will present on statistical validation for additive manufacturing at the upcoming International Conference on Advanced Manufacturing (ICAM). Learn more and register via the button below.

ICAM 2026

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Advance CNC Machining of Grove City, Ohio, says it has seen the prices of its typical solid-carbide end mills rise by 20 to 30 percent in the past year. And the higher price is not even the primary challenge. More concerning is what comes next, with cutting tool suppliers warning of additional increases to come, though amounts and timing are both uncertain. That uncertainty introduces volatility into a major cost component of machining that used to be stable, complicating quoting and long-term commitments.

In all of this, Advance CNC is not unique. What might distinguish this machining provider is the set of disciplines it has been able to bring to the problem. The company specializes in four- and five-axis CNC machining for aerospace and defense, supported by pallet-system automation. Its experience mixing ongoing production and short-lead-time jobs within these same platforms has fostered a culture that is simultaneously structured and adaptable, and thus better equipped than many manufacturing teams to respond to instability without sacrificing control.

Two of the company’s team members who have been focused on the response to cutting tool price changes are Tooling Manager Derek Maschmann (left) and VP of Machining Technology Kyle Dunaway (right). Both are helping to drive a set of evolving tactics aimed at containing the impact of this cost.

“It’s a really unstable market for carbide tools,” says Maschmann. Price increases are not even consistent from supplier to supplier. “It’s hard to make lasting decisions when you don’t know what it’s going to look like two weeks down the road.”

Yet they both also emphasize a principle that has not changed. Though tools matter, cutting tools still are not the dominant factor driving the economics or the success of any job. “Tooling is five to seven percent of your cost to machine a part,” Maschmann says. “If you can get more parts out the door, then an increase in the cost of the tool that lets you do this shouldn’t overly influence decisions.” This remains true even at higher prices.

In other words, responding to rising tool prices cannot come at the expense of throughput, control, or quality. Therefore, Advance CNC is addressing tooling cost through adaptation rather than compromise. The combination of tactics helping the company limit the impact of rising prices includes (A) more deliberately selecting tools to match machined features, (B) expanding the sourcing strategy, (C) resale of lightly used tools, and (D) exploration of alternatives to carbide. More on each below.

First, it is worth understanding how and why carbide prices began to shoot up in the first place.

Why Are Carbide Cutting Tool Prices Increasing?

The forces driving up the price of carbide tooling for a machine shop in Ohio originate on the other side of the globe. At the center of the change is tungsten, a necessary ingredient in carbide, which has become harder to source internationally. Global supply of tungsten is highly concentrated, with China dominating both mining and processing. Recent export controls by China over tungsten and other materials have constrained the supply.

Meanwhile, demand remains high. Aerospace and defense manufacturing, along with other expanding sectors such as semiconductors and the battery needs in automotive, continue to consume both tungsten and the high-performance tools made with it. Plus, cobalt—used as a binder in many carbide tool grades—is under similar supply constraints, adding to the pressure.

The result is not simply higher prices, but less predictable ones. Where cutting tool costs once tended to rise gradually and with some regularity, suppliers are now issuing increases that vary widely depending on the individual producers’ sourcing predicaments and levels of vertical integration. For machine shops dependent on cutting tools as an essential component of their work, this unevenness adds a new layer of uncertainty to the daily challenge of machining cost-effectively.

And Advance CNC accepts this shift as more or less permanent, say Dunaway and Maschmann. The volatility may settle out, but higher prices and faster-rising prices for carbide represent a structural change rather than a disruption. The economics of tooling are being reset by a combination of constrained raw materials, geopolitical influence and sustained industrial demand. That reality means lasting change is warranted as part of the ways a machining operation has to respond.

The Wrong Response: Chasing Lower Tool Cost

Faced with rising and uncertain tool prices, Dunaway says the most obvious response, to spend less on tooling, is also likely to be the least effective. Switching to lower-performance tools, running tools longer, or simplifying the tool library to reduce purchasing complexity all appear to offset higher prices, while what these steps really do is impair the success of the process and add greater cost elsewhere.

Again, tooling is five to seven percent of cost. Tooling price rises may raise that percentage. Yet other contributors to cost are apt to change to an even greater extent if the proper tooling investment is not made.

For example, lower-grade tooling saves purchase cost but brings shorter tool life, adding labor cost for more frequent tool change. Similarly, pushing tools deeper into their wear curve may extract more work from each tool on paper, but increases the likelihood of tool failure leading to unplanned intervention.

For Advance CNC, the conclusion is straightforward: Responding to higher tool prices by compromising on tooling is a losing strategy. The company’s approach instead starts by protecting what matters most—machine utilization and process stability—and then working to control cost within these constraints.

Automation as the Foundation of Tooling Strategy

As a result, any discussion of tooling for Advance CNC begins with automation. The company’s machining processes are built around palletized systems allowing horizontal machining centers to perform for extended periods of unattended operation. This kind of automation changes what matters in tooling. Because the process cannot be reliant on oversight or real-time manual process adjustment, consequential variables have to be known and controlled, including the condition of the tool.

The shop’s standard practice therefore involves beginning each production run with a new tool and replacing tools before any appreciable change in the edge. Never mind running tools to failure—there is not even any running tools to the onset of meaningful wear.

“We can’t put a tool with 80 percent remaining life into a process that might be expecting 100 percent,” says Maschmann.

This discipline obviously imposes a cost: Tools are not consumed fully; the value of a tool in terms of its usable life often is not realized. However, in an automated system, the cost of unpredictability far exceeds the value of obtaining an additional increment of cutting from any tool.

In this sense, then, the tooling strategy is not about the tool. The strategy focuses instead on assuring the integrity of the process. The tool price is not the only cost at stake, because managing tooling to preserve reliability also affects throughput, efficiency and delivery.

So how does the shop control tool cost within the constraint of these priorities, in which tool cost is not permitted to be a driving factor?

Advance CNC has found five tactics—all of them departures from the way it once sourced and managed tools—that fit the new reality of cutting tool pricing.

Tactic 1: More Deliberate Tool Selection (Less General-Purpose Thinking)

One of the clearest changes the shop has made in response to rising tool costs is a shift toward greater use of tools designed for specific operations. This shift is essentially an investment in greater time and attention for programming and tool management.

In the past, relying on general-purpose carbide tools helped make programming simpler and purchasing more efficient. But today, that tradeoff—fewer tools to keep track of, even if the tool is not the ideal choice for the cut—is harder to justify. Today, part programming more commonly goes feature by feature, with tools chosen for their suitability to each separate feature.

This has led to a broader and more specialized tool library, with individual tools selected for their ability to remove material faster, hold tolerance more consistently or last more predictably through some particular step in the machining cycle.

Some examples Dunaway cites: “We think carefully about just the right tool for small features, as well as the right tool for different cases of heavy stock removal.” In these cases, rather than doing well with an available tool that will serve the need, “We pick an optimized tool for that feature that lets us get closest to what that tool is best able to do.”

The shift places greater demands on the programmer to select just the right tool for each cut, and on tool management to oversee the expanded tooling array. Today, these investments in greater attention are prices worth paying.

Tactic 2: Multi-Sourcing and Price Leverage

Alongside technical decisions about how tools are used, Advance CNC is also addressing cost and uncertainty through new procedures in how tools are sourced. In an environment of stable tool pricing, developing a close relationship with one strong tool provider is advantageous. But in the new environment, the shop deliberately avoids becoming tied to a single supplier or even a small number of suppliers. The company instead maintains relationships with various tooling sources to maintain flexibility in pricing and availability.

The approach offers more than just a little savings, Maschmann says. The tungsten market changes are so dramatic and new that different tool manufacturers are often affected very differently by supply constraint shifts, depending on how vertically integrated they are and on their sourcing chain for the raw material. By maintaining multiple channels, Advance CNC can pivot its purchases to benefit from these differences.

Tactic 3: High-Performance Tools as a Cost-Control Strategy

Not only has it resisted lower-cost tools, Maschmann notes, but Advance CNC has in fact committed to the opposite course. The shop has shifted to even higher-performance tools in some cases, counterintuitively pursuing cost saving with even higher-price choices.

The reason is rooted in the economics of machining. If a higher-performing tool runs faster or maintains cutting performance longer within a predictable window, then those gains reduce cycle time or labor involvement. This kind of gain makes sense even in the absence of significant tool price rises. However, when the cost of tools in general is moving higher, stepping up to even higher-performing options in strategic cases becomes an expense that is more readily absorbable within the changed cost overall.

Stated another way: If tool cost is going to rise anyway, then work within that context to invest for savings in other areas.

Tactic 4: Resale of Lightly Used Tooling

This is perhaps the most extreme or unconventional of Advance CNC’s responses to rising tool cost: It has become a tool supplier.

Because the company does not run tools to the onset of significant wear, it routinely stores tools that still have significant usable life remaining. In the past, tools such as these sat in inventory waiting for some non-critical need. Today, Advance CNC recaptures cost by selling the tools. The company has launched a program to resell lightly used high-performance tooling that no longer meets the requirements of its automated processes. The tools seen in the drawer above are a sample of this lightly used inventory.

Selling these tools brings more return than carbide scrap. Meanwhile, for the shops buying the tools—smaller shops with less tooling budget—this secondary market for tooling meaningfully expands the tooling options available.

Tactic 5: Exploring Carbide Alternatives (PCD and Beyond)

Rising carbide costs are also prompting the Advance CNC team to revisit tooling options that previously held less appeal. Among these is polycrystalline diamond (PCD), a tool material that has long offered advantages in aluminum (the shop’s most common part material), coupled with cost and process considerations that are not as inhibiting as they once were.

Specifically, PCD tools have a high upfront price relative to carbide, and they require a different approach to machining. Cutting behavior and toolpath strategy differ from carbide, as does chip management, because the very fine chips produced by a PCD tool present challenges in chip evacuation over long runs.

All these differences have favored carbide in previous times, but now the balance is shifting. As carbide prices have destabilized, a tool offering long life and high performance in combination with a predictable price now warrants a careful second look.

Carbide will still be the predominant choice, Dunaway says. But he is spearheading the work of selectively evaluating where PCD cutting tools such as the one seen above can best add value within the constraints and priorities of Advance CNC’s processes. This includes assessing tool life, cutting performance, and how well a given tool can integrate into the automated workflow.

“We have to seriously evaluate where those tools make sense,” he says.

There was never a need to do this before. The assumption until now has been that cutting tools are generally carbide. Now, a lasting result of surging tungsten prices is likely to be greater application of non-carbide within a more diverse set of tooling options than what the shop was previously prepared to accept.

Implication for Aerospace and Defense Buyers

For aerospace and defense OEMs and Tier 1 suppliers, volatile carbide tool prices are a supplier-side issue significant enough to ripple into quoting and lead times, but the ripples can be damped. The question is not solely whether suppliers are affected by the cost changes, but also how they are managing the effects.

A sophisticated machining supplier has tactics to employ that operate at points of leverage other than tool purchasing. More granular application of tooling, recapture of tool cost, and even doubling down on high-performance tools all are part of the response. The result is not immunity from cost increases, but insulation from the worst of their disruption.

In this way, rising carbide prices offer a test of supplier and system resilience. In fact, because some of the tactics include shifts away from reliance on foreign-sourced material, the result might be even more transformative. By encouraging a more widespread use of alternate tool materials such as PCD, the rising carbide prices may be helping to bring about not just greater manufacturing resilience, but greater independence as well.

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SLS can feel like the costliest polymer 3D printing process because it is generally used in industrial contexts, and because the detail of the parts it produces is so fine. In fact, SLS offers the potential to deliver the lowest per-piece cost among AM processes.

A few simple tactics help to deliver on this potential. They include tweaking designs to allow for maximum packing of parts within the build volume and (this is the underappreciated cost lever) designing to avoid thick walls.

Because there is so much potential for mild design changes to bring major cost savings, the first pricing quote for SLS is often misleading. Nate and I break this down in the video.

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On the latest episode of The Z Axis, I visit Push Plastic to speak with the company’s president of global sales, Seth Hollingsworth. Push Plastic makes 3D printer filament, the long plastic strand delivered on spools that feeds extrusion-style 3D printers. 3D printer filament is often purchased on price, often regarded as a commodity, often ordered from producers in China. But Push Plastic manufactures its product with a U.S. workforce in a U.S. facility. I talk to Hollingsworth about the differences that distinguish filament and how the company succeeds against low-cost producers.

Also in this episode, we see the factory. Anyone with a desktop 3D printer will likely appreciate this view of how filament is made. Hollingsworth says he routinely confronts disbelief from new customers; they assume Push must be an importer, not a manufacturer. To help with correcting that view, I am happy to offer this visual record of 3D printer filament production being done in Arkansas:

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Cold Metal Fusion is a process for tricking a polymer selective laser sintering (SLS) machine into making metal parts.

The process was developed by Headmade Materials. CADmore Metal is the agent for the process working to develop applications for it in North America. We paid a visit to CADmore Metal for the lastest episode of Manufacturing Unlimited, sponsored by the International Conference on Advanced Manufacturing (ICAM), where CADmore Metal’s John Calhoun (pictured below) has presented this technology.

The process is timely. SLS is a well-established polymer additive manufacturing process, and small low-cost SLS machines have recently come to market from various providers. The Cold Metal Fusion process, which is like metal injection molding (MIM) without a mold, uses a binder (like in MIM) to shape the part until debinding and sintering make the metal part fully dense. The process’s demands from the machine are low, so SLS machines at all levels can be used to make metal parts with this process. CADmore currently uses larger SLS machines from XYZprinting (visible in the first of the two photos above) and smaller machines from Formlabs and Sinterit (partly visible with John and me in the second photo) to make production metal parts, primarily in titanium.

The machines run in a kitchen. That was one of the discoveries upon visiting. Calhoun says CADmore Metal launched so fast, it was short on time to find a space. A former industrial kitchen was available. The space proved to be fitting for this process, which has relatively simple workflow and handling needs. Learn more in the episode:

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Allied Cycle Works was founded on the vision of making hand-laid carbon-fiber-composite bicycle frames in the U.S. But the company discovered that an entirely different form of production, previously unseen—CNC machining—made far more sense for domestic production and vertical insourcing.

Success is actually what did in the original vision. Demand for Allied’s bikes surged during covid. But scaling in response to that demand faced tremendous friction.

As we lay out in the video above, Allied experienced the way scaling is difficult and costly if a manufacturing process is poorly located in terms of infrastructure, labor availability, and supplier proximity. It is easy to cheer for more US manufacturing, but manufacturing decision-makers have to face these realities.

Watch my conversation with Sam Pickman of Allied Cycle Works in the video above. The company never retreated from U.S. manufacturing, but it learned how to get real about it, and how to make local production a sustaining commitment.

This conversation is the first episode of The Z Axis, my new video series produced by Edge Factor in which we will thoughtfully explore one issue after another, large and small, confronting manufacturing leaders today. How to succeed with U.S. production is going to be a recurring theme. If you are a subscriber to this newsletter, you will receive the show, because I will share each episode here. But if YouTube is the platform where you would rather have video content find you, subscribe to my YouTube channel as well.

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What does “strong” mean in a plastic part? Potentially a lot. In the video below, Nate Stevens of Lifestyle Additive and I unpack that question by looking at several parts made through selective laser sintering (SLS) 3D printing.

“Strong” could refer to:

• Rigidity vs. flexibility

• Impact vs. fatigue

• Strength in a particular location of the part

• Strength along a particular direction of loading

In all these cases, part designers tend to think of strength almost exclusively as a material property because they have been trained to do so by injection molding, the usual means of producing plastic components. The part features that are the most effective at making a plastic part strong are generally difficult or impossible to mold. But the same features are straightforward to 3D print, particularly with SLS, which has no support structures to contend with.

The result: With SLS, strength is not just a material property, but increasingly also a design decision.

There is a particularly notable example to be seen in the video, a component that had to combine rigidity and spring-like flexibility within the same solid piece. Watch here:

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How do you get consistent 3D printing when the feedstock material is not consistent?

That is the challenge Austin, Texas-based 3D printer maker re:3D took on as part of the ReCreateIt project to direct plastic waste into local production.

Usually, 3D printing gets to begin with very consistent material—plastic filament or pellets.

But this application seeks a solution for local manufacturing wherever plastic waste is accumulating. The waste material has to be sent into production with only minimal onsite breakdown: shredding the discarded plastic into flake.

The flake material is inconsistent in both shape and density.

To deal with this, to turn the inconsistent plastic into repeatable production, the re:3D printer uses an extrusion system incorporating a screw like the extruder of a molding machine, but tailored to this recycled plastics application.

I filmed the video above explaining the technology (and showing the screw) during a visit to re:3D. This video is a bonus episode of Manufacturing Unlimited, the video series I’ve created with ASTM International. The main episode has much more to say about the ReCreateIt project, which is succeeding at turning plastic waste into local production at an Austin test site—a thrift store. Watch that episode here.

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For a cylindrical part made through selective laser sintering (SLS) additive manufacturing, how should the part orient within the build: lying down or pointing up?

Answer: If the roundness of the cylinder is important (as it probably is), then the answer is pointing up. The precision of SLS occurs in the X-Y plane. So let this plane produce every circular cross-section of the part, all the way up.

The set of cylindrical parts seen on in the images on this page illustrate the potential problem. The axial length of the locking features protruding at one end of one of the parts is also a critical dimension. Pointing the part up puts this dimension, the length of the feature along the axis of the part, fully parallel to Z and therefore fully removed from the X-Y plane.

Here is the answer that worked for this part: Size the key length in increments of layer height. In SLS, the layer height is the Z motion, and while this height might represent a coarse increment, it is tightly repeatable from layer to layer.

This part is a real production case produced by Lifestyle Additive, a specialist in SLS. Nate Stevens of Lifestyle Additive and I have been working on a series of videos about designing to make full use of SLS’s special capabilities. The latest video (discussing this part and others) is about how to design for SLS to fully realize its tight precision:

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A Habitat for Humanity ReStore in Austin, Texas, is the pilot site for a project funded by the National Science Foundation that employs 3D printing to realize a new model for plastics waste recycling and plastic part production.

The project is called ReCreateIt. It is the subject of the latest episode of Manufacturing Unlimited, my new video series produced by the ASTM International Additive Manufacturing Center of Excellence and sponsored by the International Conference on Advanced Manufacturing (ICAM), where this technology was presented. Story follows, and I link to the episode below.

A lot just outside the Austin ReStore is where a modular manufacturing cell (seen behind me) transforms discarded plastic into products sold in the store.

The plastic reclaimed in this way is from plastic bins—a category of plastic items this donation center receives in huge quantities and otherwise has to discard.

Through the ReCreateIt project, these bins are now ground into flake, which can be fed directly into a large-format 3D printer.

The result is stools, lampshades, vases and other products now sold in the store, made from material previously headed toward disposal. This model illustrates how waste plastic can be converted into any locally needed products or parts.

A key technology comes from Austin-based 3D printer maker Re3D. The company engineered an extrusion system capable of producing repeatable, high-quality parts using the ground plastic flake as the feedstock. Thanks to this technology, a simple plastics shredder is sufficient to convert solid plastic into additive manufacturing raw material.

Carolyn Seepersad, PhD, of Georgia Tech (appearing in the episode) was part of the team that has developed not only path strategies for 3D printing effectively with the recycled material, but also filters alerting users to geometries not likely to print well.

All this together helps make this plastics recycling and manufacturing system easy enough to use to make it practical in remote locations. While plastic has long offered the promise of reuse, the logistics of shipping the plastic to a manufacturing facility capable of reusing it have presented a persistent challenge. ReCreateIt now can offer this response: Why not ship manufacturing instead? In Austin, the 3D printer and plastics shredder operate within the “Gigalab,” the plastics-reuse factory within a shipping container.

ReCreateIt Project Director Britney Blann now leads the team aiming to reclaim 10,000 pounds of plastic waste with this modular factory. She describes the effort in the episode.

The larger vision imagines locations far from Austin. The concept might have its greatest value in communities or parts of the world where waste management infrastructure is limited. Wherever plastic is piling up, that accumulation could be the location for a modular factory. Two wins result from this: The plastic waste is reclaimed, and the community has local manufacturing capability for needed parts and products.

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The new labor bottleneck in the most advanced CNC machining facilities is brainpower to address both the daily needs and the emerging opportunities of this capability. Data interconnectivity is the answer—but interconnectivity to an extent that is radical for CNC machining.

I learned all about this during a visit to Orizon. The Olathe, Kansas, machining provider is among the most advanced machine shops in the United States. Long, sophisticated five-axis machine tools from Modig produce some of the most challenging structural components of both commercial and military aircraft.

The problem: Tolerances are so tight, and feature-to-feature precision demands are so exacting, a tiny drift from nominal in any aspect of the process—tooling, temperature, machine components, clamping—could lead to an out-of-spec part. Orizon machining experts like Project Manager Rex Kelly (seen above at right) pore through data in search of root causes where there is a quality concern.

However, this is a poor use of time, because Orizon is flourishing. Kelly ought to be developing new processes related to incoming new projects.

The answer is data interconnectivity, but an application of interconnectivity that goes way beyond machine monitoring as many machine shops practice it. Industrial interconnectivity startup Flexxbotics has new technology that translates for machines speaking unlike languages—translates the data into a shared real-time control plane allowing for shop-wide monitoring and control of the entire machining value chain.

Orizon is an early user. And a committed user. By uniting machine tools, shopfloor measurement, quality inspection, robots, tool room, shop management systems and more into unified real-time control, the automated system will be able to monitor, correlate and respond to far more data (beyond 1 million data points per day) than all of the Orizon team together could ever mine or study.

Setting control limits within this data, and refining the control limits over time as needed, will enable Orizon’s process to detect problems before they affect parts.

Quality issues will decline. Team members like Kelly will be able to turn their attention forward.

Then, the next step will be AI for predictive process control. The Flexxbotics system does not need AI, but essentially prepares the space and extends the welcome for it. In the unified control plane, the critical mass of data needed for AI is gathered together and contextualized.

At one point in our conversation, Kelly offered this comment to characterize how the shop can now perform:

This depiction quietly floored me, because I remember how—nearly 30 years ago—one of my earliest feature articles described the hope that a machine shop might one day function as an organism in this way.

That quote from Kelly appears in the video below. This mini documentary filmed from Orizon about its use of the Flexxbotics technology is the first episode of Manufacturing Unlimited, a show I am creating with ASTM International. Each episode will visit a destination where manufacturing technology is changing. Flexxbotics presented its real-time interconnectivity technology at ASTM’s annual International Conference on Advanced Manufacturing (ICAM), which sponsors this show.

Watch the full episode above to learn more about interconnectivity in CNC machining and its promise to expand capacity by setting expertise free at Orizon.

More episodes of Manufacturing Unlimited are coming! Subscribe to this newsletter and to my YouTube channel to follow the show.

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In a recent post, I described the antistatic properties of new black PA-12 nylon for selective laser sintering (SLS) additive manufacturing machines from EOS. Material developer ALM – Advanced Laser Materials set out to create a PA-12 for SLS that is inherently black and needs no dye, and along the way created a material with a set of properties not found in any other material. As Lifestyle Additive Manufacturing discovered in its evaluation of the material, this is a Class II biocompatible material that also has strong antistatic properties. That combination presents various opportunities related to medical device component production. Read more.

The video above is a follow-up to that post. Lifestyle Additive operations director Nathan Stevens conceived of multiple visual demonstrations to show the antistatic difference. To be sure, these are not scientific tests (the Big Kahuna frisbee is not lab-calibrated), but they do make clear the extent to which these two PA-12 versions differ in far more than color. Indeed, established SLS users likely have been routinely experiencing the conventional PA-12’s static without identifying the effect as such. As Stevens shows with the shop vacuum, static cling is the reason the white SLS is so apt to be sticky during cleanup. The different behavior of the black material signals an expanded range of application.

Among other things, SLS is:

• The 3D printing process most free of the need for sacrificial supports within the build.

• The polymer part-making process delivering properties nearest to injection molding (but without a mold).

• The additive manufacturing process most ready to scale to production quantities.

Nate Stevens of Lifestyle Additive and I are launching a video series all about this process. The first episode just dropped. Lifestyle Additive is a specialist in SLS; I’ve reported on their work in previous posts.

Here is first episode of The SLS Mindset, in which Stevens and I introduce SLS and where it excels:

Upcoming episodes will explore how to design SLS parts for precision; for strength; for cost saving; and for scale production.

To catch all future episodes, subscribe to this newsletter and/or subscribe to the new Lifestyle Additive and Zelinski Report channels on YouTube:

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Welcome to modern manufacturing, and the tension manufacturers will have to confront as programmed automation potentially gives way to AI-driven autonomy in manufacturing systems. The limiting of autonomy I made on my phone matches the kind of limits that might need to be defined for factories. The AI-driven autonomy in manufacturing is largely unavailable yet, at least in discrete part production, but all the pieces are advancing and it will be a possibility soon. Given that we require manufacturing to be repeatable and we expect it to be repetitive, where does autonomy in manufacturing make sense?

I recently wrote a report for ASTM International on AI and the Path Toward Autonomous Manufacturing. The basis of the report was a workshop hosted by ASTM and the University of Tennessee, Knoxville, assembling various researchers and other experts involved in the growing interconnection between manufacturing and AI. Autonomy in manufacturing was not the direct focus for most of the AI experts present, yet this promise was the potential toward which much of their work is pointing.

A pause for defining terms. Autonomy and automation are, unfortunately, words so similar that at a distance we risk blending the concepts together. They are different, and rather starkly so. Automation, at its best, captures and expresses the knowledge of human programmers into a process that can run as well as if the human were directing every movement of the system. But “autonomy” describes a system that is prepared to run better than this—by finding its way, testing the rules, and implementing improvements so that the operation is done better or performed more efficiently than the way the same system had done the same work previously. Autonomy takes automation where it currently cannot go.

In the report, I characterize the difference and the questions autonomy raises in this way:

For manufacturing, fully autonomous production is the clearest promise of AI. Indeed, autonomy is the feature distinguishing AI’s coming role in automation from automation as manufacturing has understood it until now. In this vision for AI’s role, manufacturing systems adapt, learn, and make decisions about how to produce a part and how to improve production. But autonomy raises questions and challenges. In a production environment built on predictability and traceability, how much autonomy is acceptable? And where it is acceptable, how do we provide manufacturing systems with this capability?

As I say elsewhere in the report, “The idea of machines making decisions on their own raises obvious questions about how such systems can be trusted.”

At first blush, we in manufacturing might be apt to say we never want autonomy in this way, offering this kind of unpredictability. But that goes too far. Manufacturing is not just the physical operation but also the process development that preceded it, and there are cases in which we might want AI in this role.

For example, robots in the role of “AI artisans” in manufacturing could speed, simplify and improve upon cases where manufacturing connects to human craft today, as described by Mike Groeber, a faculty director of manufacturing research at The Ohio State University:

In manufacturing, where is autonomy acceptable? The answer partly lies in tasks where humans currently perform unpredictably because they rely on experience, interpretation or judgment. Polishing a part with varying geometry, welding a feature that changes shape from part to part, and adjusting machining strategy based on chip behavior are all situations that have this characteristic of making decisions, sometimes different decisions at different times, based on observation. Teaching machines to handle situations of this type is one of the promising applications of AI.

Groeber illustrated this possibility through work underway at Ohio State’s Artificially Intelligent Manufacturing Systems (AIMS) lab. Researchers there are developing robotic systems that function as what he calls “AI artisans.” The robots are equipped with multiple manufacturing tools in the form of functional end effectors plus a forging press within the robot’s reach, and the system overseeing the robot is trained through simulation to select the sequence of operations needed to produce a part. The system acts within an observe–orient–decide–act (OODA) loop, adapting its actions based on the outcomes it encounters.

These systems operate within defined boundaries, Groeber notes. Because autonomous behavior cannot be predicted, safeguards must be built around it. Monitoring systems can constrain the range of acceptable outcomes, ensuring that AI-driven actions remain within limits established by physics, process knowledge or safety consideration. In other words, autonomy in manufacturing will not bring or entail unlimited freedom for machines, but instead will involve AI operating inside guardrails defined by human engineering understanding.

Within these guardrails, the goal of AI is to extend automation, giving manufacturing systems the ability to respond intelligently to situations too complex or variable to be fully programmed from human knowledge.

That goal has not been reached yet. We don’t need those guardrails yet. But the conversation is important and the need is apt come suddenly because the capability for manufacturing autonomy is not what is missing. Rather, enough data to be sufficient for autonomy is what is missing—and not the mass of data in terms of what is being measured, but simply the amount of data within a proper context for AI to make full use of it.

As my report notes:

Richard Huff, the Director for Industry Consortium and Partnerships with ASTM International, described related work focused on developing high-quality materials data for additive manufacturing. Generating such data is laborious. It requires collecting process parameters, machine data, in-situ monitoring results, heat-treatment information, test coupon data, and materials characterization results. Even within a single organization, assembling these datasets can be expensive and time-consuming. Across companies or machine platforms, the difficulty multiplies. A system for data sharing not only creates a valuable context for datasets, but also reduces the material testing work by providing data access and visibility to potentially reduce redundant testing.

The challenges around data help explain why many AI projects in manufacturing remain in what one speaker described as “pilot purgatory.” Yan Lu of NIST cited research suggesting that only a small fraction of industrial organizations have successfully scaled AI applications in production environments. In many cases, the limiting factor is not the algorithm but the underlying data infrastructure.

Another challenge involves context. Manufacturing data rarely speaks for itself. Without sufficient metadata describing the conditions under which it was generated, even large datasets can be difficult for AI systems to interpret correctly.

Data describing one variety of manufacturing—additive manufacturing—is particularly valuable, because additive is the manufacturing process nearest to needing AI and being able to benefit from it. I have more to say on that point in this article about AM and AI.

Data gathering and formatting thus comprise the foundational ground game of preparing today for an AI-enabled future of manufacturing. As I say in the report, the “preparation begins with capturing data consistently, preserving the context that gives it meaning, and structuring it so it can be shared across systems.” That these data disciplines generally do not characterize manufacturing today is the chief impediment to AI-enabled and therefore autonomous production. But once there is sufficient quantity and quality of data to train and empower autonomous manufacturing systems, the other components for realizing this promise are liable to be in place or not far off.

My report identifies other gaps or obstacles in the way of realizing AI’s full promise for manufacturing, including one significant misunderstanding. Multiple experts at the workshop referred to this disconnect: C-level executives in manufacturing organizations tend to associate AI and its behavior with large language models (LLMs), notably ChatGPT. That is, generative AI. By contrast, AI in manufacturing will be correlative. I describe why the misunderstanding is sufficiently unhelpful to amount to an impediment:

Manufacturing is not primarily a text environment. Rather, it is an environment of sensor signals, machine parameters, images, material measurements, quality records and process histories. Thus, the form of AI most likely to matter in production is not AI that generates language or content, but AI that can detect patterns, correlate variables, interpret physical conditions and support decisions inside complex process environments. In other words, AI in manufacturing will be less about generation and more about recognition, inference and response.

In short, manufacturing’s variety of AI will not be prone to the “hallucinations” or generative falsehood an LLM might create. Autonomy in manufacturing does mean unpredictability in the sense of processes that test and update their paths or sequence of operation, but not unpredictability at the level of what we sometimes see in our office desktop uses of AI. As AI increasingly takes its place in production, this nuance about the nature of manufacturing AI will become important to see.

Read the complete report here:

Get report: AI and Autonomous Systems

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I’ve now co-authored an article with Nate and Leo Stevens of Lifestyle to report on the black PA-12 benefits. The benefits include (A) improved forgiveness for dimensional precision and (B) antistatic behavior significantly different from white nylon.

In this video, I talk about the precision advantage. Both SLS materials are capable of fine detail 3D printing, but the black material allows the outcome to be achieved with less process finesse and/or fewer iterations. Some test panels in the video make this point.

Watch the Video

The antistatic advantage is the benefit I delayed reporting, as Lifestyle waited on the results of independent tests. It was clear to the team the black material responded differently to static charge to some extent. While the white nylon powder has notorious “static cling,” just a tap of a black part easily allows any loose black powder to fall away. But the precise extent of the effect had to be measured.

Initial testing results are now in. From the article, here is what can now be said about the electrostatic behavior:

Independent testing by an ISO-accredited laboratory confirmed the effect, measuring the material’s resistivity at approximately 10¹¹ ohms per square (a standard unit of measuring resistivity). This measurement places the black PA-12 within the category of “antistatic” materials. Its resistivity is significantly less than standard PA-12 (which behaves as an insulator), but not as low as static dissipative or ESD-safe materials.

Charge decay testing further clarified this distinction. Under a standard test condition, the material discharged from 5,000 volts to 10% of that charge in around 4 seconds. This represents a clear improvement over standard nylon, but does not meet the under-1-second requirement associated with certain ESD standards.

In other words, the material reduces static buildup and allows charge to dissipate, just not rapidly enough for the most sensitive electronic environments requiring certified ESD-safe materials.

This level of performance is meaningful. Many applications do not require full ESD compliance, but do benefit from reduced static effects. In medical and pharmaceutical contexts, static charge can influence the behavior of fine powders or atomized fluids. The ability to reduce static in a Class II biocompatible material can improve consistency in particle size and distribution in devices in which this affects dosing and performance.

Similarly, in general electronics-related applications, the material represents an improvement over commonly used plastics such as ABS or acrylic, which are fully insulative. While not a replacement for ESD-safe materials where those are required, black PA-12 can provide a practical intermediate option, especially when combined with its other advantages for precision and processing.

The point about this being a biocompatible antistatic material is significant. Electostatic discharge (ESD)-safe materials for SLS are available, offering even less resistivity, but the material is not necessarily suitable for a medical application. This material is.

Could the new black PA-12 nylon itself meet the standard for being ESD-safe? Maybe. The sample tested missed this level, but not by an uncrossable gap. Because the antistatic property is almost certainly a result of the carbon in the black material, it may be that different arrangements of carbon could produce different effects. Different printing parameters might produce parts with different resistance, including resistance in the ESD-safe range. This is speculation, but it describes the future course of testing Lifestyle will follow.

Since posting the video on precision, more has come to light on this effect as well. In addition to being forgiving of fine dimensional detail in regard to the laser, the material is similarly forgiving in regard to physical cutting. Experience has shown the material to be clearly more machinable than the white SLS nylon, simplifying postprocessing if added work is necessary to get to a fine tolerance.

Read more about the current understanding of the black PA-12 nylon in my article with the Lifestyle team:

BLACK MAGIC: BENEFITS OF NEW SLS PA-12

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