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A Characterization of 3D Printability
Ioannis Fudos, Margarita Ntousia, Vasiliki Stamati, Paschalis Charalampous, Theodora Kontodina, Ioannis Kostavelis, Dimitrios Tzovaras, Leonardo Bilalis
TL;DR
Differences among AM technologies can make printed parts depart from CAD models and compromise accuracy, finish, robustness, and functional or geometric requirements. The paper proposes a machine-specific printability score based on mesh complexity and part characteristics; benchmark tests report failures for thin features in lower-scoring models and robust printing of B3 across technologies.
Problem
Differences in AM processes, machines, and materials can make fabricated parts depart from CAD models, raising concerns about accuracy, finish, mechanical properties, and requirements.
Source: §1 · p. 1Method
The paper maps model mesh complexity and part characteristics to a technology-specific score expressing the probability of robust and accurate printing.
Source: §1 · p. 2Results
B1's low score corresponded to thin-part breakage on all machines, B2's relatively low score to thin-wall failures, and B3's better score to robust printing on all technologies.
Source: §5 · pp. 14–15Takeaways & Limitations
The score can help select a suitable 3D printing technology for a model or guide redesign for printability.
Source: §1 · p. 2Future work will evaluate more part characteristics, further validate benchmark volumes using photogrammetry and laser scanning, and adapt the score to other technologies and design intents.
Source: §6 · p. 16
Abstract
from arXiv · showhide
Additive manufacturing technologies are positioned to provide an unprecedented innovative transformation in how products are designed and manufactured. Due to differences in the technical specifications of AM technologies, the final fabricated parts can vary significantly from the original CAD models, therefore raising issues regarding accuracy, surface finish, robustness, mechanical properties, functional and geometrical constraints. Various researchers have studied the correlation between AM technologies and design rules. In this work we propose a novel approach to assessing the capability of a 3D model to be printed successfully (a.k.a printability) on a specific AM machine. This is utilized by taking into consideration the model mesh complexity and certain part characteristics. A printability score is derived for a model in reference to a specific 3D printing technology, expressing the probability of obtaining a robust and accurate end result for 3D printing on a specific AM machine. The printability score can be used either to determine which 3D technology is more suitable for manufacturing a specific model or as a guide to redesign the model to ensure printability. We verify this framework by conducting 3D printing experiments for benchmark models which are printed on three AM machines employing different technologies: Fused Deposition Modeling (FDM), Binder Jetting (3DP), and Material Jetting (Polyjet).
1 INTRODUCTION
Additive manufacturing offers design freedom, but machine and process differences can make printed parts diverge from CAD models and compromise quality or requirements. This paper proposes a technology-specific printability score based on model complexity and part characteristics, and tests it on three AM technologies.
1 INTRODUCTION
- AM process and machine differences can make fabricated parts diverge from CAD models in accuracy, finish, mechanical properties, and functional or geometrical requirements.Prior work also connects AM technologies with design rules and guidelines for model complexity and process-specific constraints.Source: §1 · pp. 1–2
- The proposed printability score estimates the probability of robust, accurate printing on a specific machine using mesh complexity and part characteristics.Design-rule parameters that affect manufactured quality are mapped to probability functions.Source: §1 · p. 2
- The score can help select a suitable 3D printing technology for a model or guide redesign for printability.Source: §1 · p. 2
- The framework is tested on benchmark models printed using FDM, Binder Jetting (3DP), and Material Jetting (Polyjet).Source: §1 · p. 2
- The framework can be parameterized or extended to estimate printability for other AM technologies.Source: §1 · p. 2
2 RELATED WORK
Related work treats model complexity through several geometric and CAD-oriented measures, and develops design rules for particular AM technologies. This paper instead uses design rules as post-design evaluation guidelines for technology-specific printability.
2 RELATED WORK
- CAD model complexity concerns a component’s features and their relationships, whereas geometrical complexity refers to basic elements such as points, lines, and surfaces; Forrest also identifies combinatorial and dimensional complexity.Source: §2 · p. 2
- Other complexity measures include algebraic, topological, morphological, and combinatorial properties, with mesh subdivision trading more vertices for smoother curves.Reducing mesh vertices can simplify a model but may also reduce accuracy.Source: §2 · pp. 2–3
- Geometric complexity has also been estimated from surface counts, STL triangle counts, or component-to-bounding-box volume, while irregular, thin, or curved features affect print time and final form.Source: §2 · p. 3
- Design-for-AM research defines technology-specific rules and principles, including reviews, rule modules, geometric catalogues, and feature-based part orientation [4, 11, 9, 3, 24].Source: §2 · p. 3
- A dfAM worksheet reduced print failures during conceptual or CAD design, whereas this work evaluates printability after design and uses rules to guide prediction and decision-making.The evaluation could also inform redesign for a specific machine.Source: §2 · p. 3
- The printability evaluation considers STL triangle count, volume deviation from the initial CAD model, wall thickness, self-supporting angles, and holes.Source: §2 · p. 3
3 MODEL AND PART CHARACTERISTICS THAT AFFECT PRINTABILITY
This section characterizes printability through the model mesh representation and design characteristics, using geometric primitives and benchmarks to examine those factors.
3.1 Geometric Primitives and Benchmarks
- The evaluation uses spheres, cylinders, tori, and rectangular parallelepipeds plus benchmarks B1–B3 designed to probe feature limits, surface quality, accuracy, functionality, and connectivity.B1 includes supported and unsupported walls, bridges, holes, and embossed and engraved details; B2 includes overhangs and connecting parts; B3 combines basic geometrical features.Source: §3.1 · p. 4
3.2 Mesh Complexity
- Mesh complexity measures the polygonal mesh submitted for slicing and printing, with convex polygons counted through triangulation.The mesh is the CAD model representation exported for machine processing.Source: §3.2 · p. 5
- At 168 faces, sphere triangulation reduces volume by over 8%; at 148224 faces, loss asymptotically reaches 0%.Higher mesh resolution reduces deviation from the initial CAD model, leading to better results in the final printed model.Source: §3.2 · pp. 5–6
3.3 Design Rules
- For overhangs above 45° FDM requires supports, whereas powder-bed printing can rely on the powder for support.Support construction can add printing cost and post-processing labor.Source: §3.3 · p. 7
- Technology-specific rules assess walls, holes, pins, Boolean overlaps, overhangs, relief detail, supports, and tolerances for connected or moving parts.These characteristics must conform to manufacturing-accuracy limits to support printability.Source: §3.3 · p. 7
4 A CHARACTERIZATION OF 3D PRINTABILITY
The printability score estimates the probability of a robust, accurate print by combining technology and mesh-related risks with risks from part characteristics. Its parameters let designers weight those risks for an application and set acceptable score thresholds.
4 A CHARACTERIZATION OF 3D PRINTABILITY
- The overall printability estimate combines technology-and-mesh success probability with the probability that each part characteristic will permit successful printing.The resulting measure is a score for a particular model and printing technology.Source: §4 · p. 9
4.1 Global Probability Function
- Technology-characteristic defect scores are mapped to probabilities, adjusted for application needs and mesh quality, with support-construction scoring exempt from mesh-resolution adjustment.The scoring schema may be altered for application requirements; QSCM is based on the ratio of mesh surface area to original CAD surface area.Source: §4.1 · pp. 9–10, Table 4 · p. 10
- For a 30 mm sphere, the 168-triangle mesh has a slightly lower global probability value than denser meshes at k=0.1, with larger differences at k=0.5.The difference is especially evident for FDM; Table 5 presents the sphere mesh comparisons for both k settings.Source: §4.1 · p. 10, Table 5 · p. 11
4.2 Part Characteristic Probability Function
- Part-characteristic flaw risk depends on technology-specific dimension thresholds and designer-set application significance; violated design rules raise failure probability.A characteristic’s significance reflects its effect on product functionality and robustness, and support characteristics also use the affected surface-area ratio.Source: §4.2 · pp. 10–11
4.3 Setting Parameters and Thresholds
- Designers set application sensitivities and the impact of each characteristic, then choose an unprintable-score threshold based on user needs and printing cost.The text gives a score of 75% as one failed print in four, while a 30% threshold may be acceptable for extremely complicated prototypes.Source: §4.3 · pp. 11–12
5 VALIDATION OF THE PRINTABILITY MEASURE THROUGH TEST CASES
The three-machine validation compares predicted printability with observed accuracy, robustness, and surface quality. Benchmark outcomes varied by model, while measured accuracy and finish also differed across technologies.
5 VALIDATION OF THE PRINTABILITY MEASURE THROUGH TEST CASES
- Tests used FDM, 3DP, and Polyjet; primitives were printed five times per machine and benchmarks three times, with scores calculated before printing.Parts were assessed for dimensional accuracy, structural robustness, and surface quality.Source: §5 · p. 12
- Polyjet parts were most accurate in both XY and Z, while Binder Jetting primitives had the smallest volume deviation from the original models.FDM parts were generally larger in both directions; 3DP parts were smaller on XY and larger on Z.Source: Figure 7 · p. 13, §5 · p. 13, Figure 8 · p. 14
- B1 bridge supports could not be cleaned without collapse on FDM and two of three Polyjet parts; 3DP bridges broke during post-processing.B2 also had technology-specific failures: overhangs printed poorly on FDM, cleaning failed on 3DP, and thin features broke, warped, or cracked.Source: §5 · pp. 13–14
- FDM parts were rough and anomalous, 3DP parts slightly porous but detailed, and Polyjet prints smooth with good detail.Source: §5 · p. 14
- For geometric primitives, score patterns matched observations: all four were printable, though the FDM sphere showed surface abnormalities.The sphere’s FDM score indicated a higher probability of printing errors.Source: §5 · pp. 12, 14
- B1 had a low printability score, B2 scored higher but remained relatively low, and B3 scored much better and printed robustly on all technologies.B1’s thin parts broke across machines, while B2 had thin-wall failures.Source: §5 · pp. 14–15, Figure 9 · p. 15
6 CONCLUSIONS
The paper proposes a technology-specific printability score based on model complexity and part characteristics. It is intended to support technology choice or redesign, with further evaluation and validation identified as future work.
6 CONCLUSIONS
- The proposed approach maps model complexity and part characteristics to technology-dependent parameters and functions that form a linear printability score.The measure is evaluated using worst-case printing scenarios.Source: §6 · pp. 15–16
- The score can help select a suitable 3D printing technology for a model or guide redesign for an intended technology.Source: §6 · p. 16
- Future work will evaluate more part characteristics, validate benchmark volume ratios with photogrammetry and laser scanning, and adapt the score to other technologies and design intents.Source: §6 · p. 16