AI in metal 3D printing data preparation
RECODE.AM #23
Build preparation - the manual process of preparing data for 3D printing - has long been one of the greatest challenges in additive manufacturing. This stage requires not only experience but also intuition and a deep understanding of the material, the machine, and the geometry of the part.
Since the early days of metal AM, a 3D printer operator had to manually analyze models, orient them on the build platform, add support structures, and assess risks related to potential deformations. All of this made the data preparation phase time-consuming, costly, and prone to human error.
In this context, artificial intelligence enters the stage as a digital assistant to the engineer - capable not only of accelerating repetitive tasks but also of bringing an entirely new level of insight into data analysis.
Machine learning–based tools can automatically identify geometric issues, suggest optimal orientations, and fine-tune process parameters based on experience drawn from thousands of previous builds. At this point, there is no talk of replacing the human engineer, but rather of significantly upgrading the efficiency and precision of their work.
One of the most important applications of AI in metal additive manufacturing is the automatic detection and correction of geometric problems.
Such systems act as true 3D diagnosticians, analyzing every surface of a model in search of anomalies that could disrupt the printing process. Thin walls that may appear acceptable to the human eye can, in fact, be too fragile to survive the metal powder melting stage. Machine learning algorithms can not only identify such areas but also suggest automatic wall thickening or localized reinforcement of the structure.
Another example involves overhangs - regions with excessive angles that tend to collapse without adequate support.
In a traditional workflow, an engineer would need to manually inspect the geometry and add supports. AI performs this task faster and more precisely, comparing the model against a database of successful prints and determining which overhangs are critical for a given machine and material. The same principle applies to channels, holes, and internal structures - AI can predict which of them are likely to deform during printing and which will remain within dimensional tolerance.
Software companies such as Dyndrite are developing advanced geometry engines that allow real-time analysis and modification of extremely complex 3D models. This approach enables AI not only to detect problems but also to simulate the effects of corrections, drastically shortening the iterative cycle between design and build preparation.
A second key area where artificial intelligence plays an increasingly important role is process parameter optimization.
Traditionally, printing parameters - such as laser power, scan speed, or layer thickness - were defined globally for the entire part. AI changes this paradigm by enabling parameter variation within a single model.
For instance, thicker layers can be applied to interior regions to speed up the build, while thinner layers can be used on external surfaces to improve accuracy and surface finish. The system analyzes local geometric features and automatically assigns optimal parameter sets to each section of the part.
An even more advanced concept is generative scan strategies. Instead of relying on simple, linear laser paths, AI can design custom beam trajectories that minimize residual stress and improve material density.
Through machine learning, the system learns which strategies yield the best results depending on alloy type and layer thickness, effectively merging thermal - mechanical simulation with real process data to enhance repeatability and predictability.
A crucial component of this workflow is the Digital Twin - a virtual replica of the printing process.
AI can be trained on data originating from FEA simulations, stress and thermal deformation analyses, as well as sensor data gathered during real builds. Predictive models learn to forecast how a given combination of parameters and part orientation will influence the final quality of the part. As a result, the system can anticipate potential defects - such as porosity or microcracks - before the first layer is even printed.
Another promising direction involves recommendation systems based on geometric similarity.
AI analyzes a new part and compares it with a database of previously printed components, identifying those with similar shapes or functions. Based on this comparison, it suggests process parameters that have led to successful prints in the past. This approach not only accelerates setup for new jobs but also helps standardize processes across an entire organization.
The benefits of applying AI in build preparation are tangible and measurable. The time required to prepare a single part can drop from hours or days to mere minutes. Reducing human errors and failed builds directly translates into savings in material, energy, and machine time.
Of course, integrating AI into the build preparation process also presents challenges.
The quality and diversity of training data have a tremendous impact on model accuracy. A system trained on a limited dataset may struggle to generalize to new geometries or materials. Another issue is the so-called “black box” effect - users may find it difficult to understand why the algorithm recommended certain settings or corrections. For this reason, model transparency and the ability for humans to verify system decisions are essential.
Equally important is system integration - AI must work seamlessly with existing CAD, CAM, and production management systems, as well as with the printers themselves. Once again, companies like Dyndrite emphasize open architectures and interoperability as the foundation for effective AI-driven additive manufacturing workflows.
Ultimately, the goal of this evolution is to increase the predictability, repeatability, and profitability of metal 3D printing, paving the way for its full adoption in serial production.
Artificial intelligence does not replace the engineer - it empowers them, transforming data preparation from a manual craft into a high-level, automated, and intelligent process that drives the future of digital manufacturing.