Imagine that every part leaving a production line carries with it an invisible, additional charge—not for material or energy, but for the frustration and wasted time of machine operators.
This, unfortunately, is the daily reality for many industrial facilities that eagerly embraced additive manufacturing.
A technology that once promised speed, flexibility, and design freedom increasingly encounters a barrier that is not immediately visible.
3D printing is no longer confined to prototyping; it is steadily becoming a component of full-scale serial production. Aerospace, medical, and automotive industries are beginning to treat additive machines on par with milling machines or lathes.
Yet, with scale come new challenges. While managers and engineers focus on print quality, machine parameters, and process efficiency, the real bottleneck lies earlier—in the preparation of data for printing.
It is here, at the level of the digital workflow, that a “hidden tax” emerges, one capable of undermining the profitability of an entire venture.
What is the “hidden tax” of data preparation?
The costs of data preparation are not as tangible as electricity bills or invoices for metal powder. They are opportunity costs: the time of highly skilled operators, delays in project delivery, underutilization of production machines, and the risk of human error during manual tasks.
In many companies, this process is perceived as a natural part of the production cycle—something “unavoidable.”
The hidden nature of this “tax” comes from its dispersion. It does not appear in unit cost reports and cannot be seen as a separate line item on financial charts. Instead, it is absorbed into engineers’ salaries, unplanned overtime, or the downtime of machines waiting for prepared files.
Managers often lack full awareness of its scale, as these costs are buried in organizational structures. Yet when viewed from the perspective of overall production, this invisible burden can outweigh the cost of raw materials themselves.
Scale reveals the true cost.
At the level of rapid prototyping, the hidden tax may appear negligible. Preparing a single unique part—even if it takes an hour—fits within the logic of an R&D project. No one counts every minute spent setting supports or manually fixing geometry. The cost of data preparation is absorbed by the enthusiasm of innovation and the rhythm of experimentation.
The situation changes dramatically, however, when a prototype becomes a product, and a single print must be replicated hundreds or thousands of times. Imagine a series of 1,000 parts, each requiring the same preparation process.
One hour of manual operator work suddenly becomes 1,000 hours—over half a year of one person’s full-time effort.
In such a scenario, the cost of data preparation far exceeds the cost of printing itself, while machine efficiency plummets.
At this stage, many companies discover that moving into serial production does not guarantee economies of scale. Quite the opposite—without automation in data preparation, every new part carries an additional burden. The “hidden tax” becomes the single greatest barrier to profitability and repeatability in additive processes.
Why is data preparation so inefficient?
There are several root causes, all systemic in nature.
The first is manual, repetitive tasks. Operators must reposition, rotate, duplicate, or nest parts repeatedly. Often, there is no way to save or replicate workflows, leading to the same actions being performed hundreds of times.
The second is the so-called polygon mesh trap.
Standard file formats such as STL or 3MF reduce complex models to sets of triangles. While sufficient for prototyping, this approach fails to meet the requirements of serial production. Every modification—from support adjustments to mesh repairs—is error-prone and time-consuming. The result is a barrier separating design from efficient implementation.
The third source of inefficiency is closed software systems that function as black boxes. They provide limited features and prevent integration with external tools. As a result, organizations are forced to operate within rigid frameworks dictated by software vendors, making it impossible to build fully automated workflows.
Given this context, it is no surprise that demand is growing for next-generation solutions that open access to parametric CAD geometry and enable deeper process automation.
One such example is Dyndrite, whose geometry kernel interprets design intent rather than merely managing polygons. This approach reshapes engineering workflows, enabling operations that were once laborious and error-prone.
How to escape the hidden tax of AM?
The first step is moving away from mesh-based files toward native data formats that retain full design intent. This enables fast, repeatable operations that previously required hours of manual work.
Next-generation geometry kernels, such as Dyndrite ACE, allow processes to be built around design intent rather than the limitations of STL.
The second pillar is automation through scripting and open APIs. Instead of recording macros that mimic user clicks, engineers can write Python scripts to fully control data preparation.
A single script can handle a thousand parts, ensuring identical parameters and eliminating human error. Moreover, such workflows can be adapted across machines, materials, and geometries—ensuring scalability and repeatability.
The third element is the adoption of a “set-it-and-forget-it” philosophy. In this approach, the data preparation process is fully defined, saved, and automated for a given part.
Instead of spending hours on each new job, operators run a ready-made workflow with a single click. Their work shifts to verifying results, while machines run without unnecessary downtime.
In practice, this frees companies from the hidden tax. Many adopters report reductions in data preparation times from several hours to just minutes. Solutions from Dyndrite and other providers of digital process automation show that the path to mass additive production runs not only through new materials and faster machines but also—perhaps above all—through intelligent software.
A case study
Consider a manufacturer of thermal nozzles for the aerospace industry. The company receives an order for 500 identical parts per month.
Using traditional methods, preparing data for one batch takes an operator eight hours—time required to position geometry, set supports, and optimize layout.
The decision to adopt software with open APIs and scripting changes the situation entirely.
Engineers create a dedicated workflow for this geometry, which can then be run automatically for each new order. Preparation time for a batch drops from eight hours to just fifteen minutes—a 97% reduction in labor costs.
As a result, the company fully utilizes its machines, avoids downtime and overtime, reduces human error, and ensures repeatability as a guarantee of quality.
This is a hypothetical scenario, but one very close to real implementations by enterprises adopting modern tools.
Conclusion
Additive manufacturing stands at a crossroads. On one side are faster machines, new materials, and ambitious industrial projects. On the other, an invisible but very real burden of data preparation that undermines profitability. The “hidden tax” is not an abstract idea but a daily cost that grows with scale.
If 3D printing is to truly enter production halls and compete with traditional manufacturing technologies, software must keep pace with hardware.
Investment in advanced, open, and automatable data preparation tools is no longer a luxury—it is a necessity. Solutions built on powerful geometry kernels and scriptable workflows, such as those developed by Dyndrite, prove that it is possible to shed this burden and unlock the true potential of additive manufacturing.
It is time to stop accepting the “hidden tax” and start investing in tools that eliminate it.