A novel method to simulate radiographs of 3D printed objects
Bibliographic record
Abstract
BACKGROUND: 3D printing has a number of applications within medicine and healthcare. In applications involving radiography, the internal infill structure and external geometry of a 3D printed part can produce undesirable artifacts, limiting the full potential of 3D printing as a manufacturing technology. While the mechanical performance of a 3D printed part can be easily simulated, it is difficult to simulate the radiographic artifact produced. PURPOSE: The purpose of this work was to develop a tool that allows users to simulate the radiographic artifact produced by a 3D printed object. METHODS: Three regular hexagons of identical geometry were sliced and 3D printed using polylactic acid (PLA) filament on a fused deposition modeling (FDM) 3D printer with varying infill patterns: rectilinear grid, cubic, and gyroid. The hexagons were then radiographed using clinical-standard scanning protocols. The captured radiographs were compared to simulated radiographs generated using the G-Code developed when the objects were sliced. The physical and simulated virtual radiographs were compared to one another, and the simulated angle of least and greatest artifact was noted. RESULTS: Strong visual agreement was found between the physically captured and simulated virtual radiographs. The projection angles that produced the least amount of artifact were 22.5°, 22.5°, and 12.25° for grid, cubic, and gyroid infills, respectively. The projection angles that produced the greatest amount of artifact were 0°, 45°, and 45° for grid, cubic, and gyroid infills, respectively. CONCLUSIONS: This work provides designers of 3D printed components with a new way to evaluate a design's radiographic performance. Previously, designers would have to physically print and radiograph a part to determine the artifact produced. This work outlines the development of a tool that simulates the radiograph of a 3D printed part from multiple different projections, saving designers time to iterate to their final design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".