The “Last Mile” problem a.k.a. the illusion of the “Print” button
RECODE.AM #26
The world of 3D printing has long been driven by the seductive vision of fully automated manufacturing: press “print,” and a ready-to-use model emerges almost out of thin air.
And while the 3D printing process itself is now largely automated, one crucial stage is often overlooked - post-processing.
Except for the simplest geometric models, this stage remains the greatest source of unpredictability, cost, and delay.
Paradoxically, in the age of intelligent slicers, it is precisely this “last mile” of the process that turns out to be the most analog.
The paradox of technological maturity
The term “Last Mile” is borrowed from logistics and telecommunications, where it describes the final, most difficult, and most expensive stage of delivering a product to the customer. In the context of 3D printing, this “last mile” refers to everything that happens after printing is complete - the entire chain of operations leading to a finished, ready-to-use component.
It is precisely here that a significant portion of additive manufacturing’s potential savings and technological advantages are lost.
While the software for data preparation and the manufacturing process itself are already technologically mature, the lack of digital integration in post-processing remains a major bottleneck to scalability.
The true value of software, therefore, lies not in making 3D printing quick, easy, and convenient, but in the ability to harmonize all stages of production – from design to quality control.
Why is the “Last Mile” such a challenge?
The first and most obvious issue is physical unpredictability. Support removal, though seemingly simple, requires force, experience, and finesse. Excessive force can damage delicate surfaces, while incomplete removal of supports necessitates additional manual finishing.
Each material and geometry behaves differently, and accurately predicting the “residual strength” of supports remains beyond the reach of most simulation systems.
Heat treatment, essential for many metal applications, adds another level of complexity - under pressure and temperature, parts may deform, and the material’s microstructure and mechanical properties change.
Machining, in turn, faces the challenge of mismatch between the ideal CAD model and the rough, as-printed surface. This leads to difficulties in positioning and, in some cases, even makes proper fixturing on a CNC machine impossible.
A second barrier is ecosystem fragmentation. 3D printers, materials, heat treatment furnaces, CNC centers, and robots often come from different suppliers, communicate in different “languages,” and lack common data exchange standards.
As a result, information about print orientation, stress distribution, or support density is not passed along the chain, even though it could significantly improve process planning.
Production data remain trapped in so-called silos - each piece of software knows something, but none knows everything.
The third factor is hidden cost. Manual labor, preparation time, the risk of operator error, or part damage during post-processing can easily outweigh the benefits of additive manufacturing at scale.
A single careless strike with a chisel can destroy an expensive part, forcing an entire batch to be reprinted from scratch.
Current approaches and their limitations
Today, the dominant model for managing post-processing is manual planning based on operator experience.
Each facility develops its own instructions and procedures, often in the form of static documents that are not integrated with production data.
Such systems are not only prone to error but also lack repeatability - reproducing identical process conditions in the next production run is nearly impossible.
As a result, part quality may vary even when printing parameters remain the same.
Some manufacturers attempt to address this issue by creating so-called islands of automation. Specialized applications are developed for toolpath generation in CNC machining, software for HIP process optimization, or autonomous robots for support removal.
However, these tools operate in isolation - each optimizes its own part of the process without access to upstream data. Consequently, decisions are made in an informational vacuum, leading to compromises and suboptimal results.
It’s like an orchestra where each section reads from a different score - the music may be technically correct, but it will never be truly harmonious.
Development directions
The next generation of additive manufacturing software is moving toward full data integration within what’s known as the digital thread - a continuous flow of information connecting every stage of a product’s lifecycle.
A key element of this transformation is design for additive manufacturing with post-processing in mind, or DFAM-PP. This means generative algorithms take into account not only structural strength and weight but also CNC tool accessibility, ease of support removal, and predictability of heat distribution in the furnace.
This makes it possible to design parts that are not only printable but also automatically processable.
The next step is intelligent process planning, or process orchestration. Instead of generating separate files and instructions, integrated production platforms can create digital data sets for the entire chain - from robotic toolpaths for support removal to heat treatment recipes and CAM files based on the part’s actual scanned geometry.
In practice, this means the software not only predicts how a part will deform during printing but also knows how to compensate for those deformations during heat or mechanical processing.
Toward “One-Click Production”
For 3D printing to become a truly industrial production process, investments must shift from the printing stage itself to the entire value chain.
The vision of “One-Click Production” describes an environment in which software automatically selects parameters, plans post-processing, simulates deformations, and coordinates all operations required to deliver a finished product.
Only in such an ecosystem can additive manufacturing fulfill its promise of an industrial revolution. Yet as long as the last mile remains manual, costly, and unpredictable, AM will remain primarily the domain of prototyping rather than serial production.