Cost-effective micro-CT system for non-destructive testing of titanium 3D printed medical components
Bibliographic record
Abstract
Micro-CT imaging can be used as an effective method for non-destructive testing (NDT) of metal 3D printed parts-including titanium biomedical components fabricated using laser powder-bed-fusion (LPBF). Unfortunately, the cost of commercially available micro-CT scanners renders routine NDT for biomedical applications prohibitively expensive. This study describes the design, manufacturing, and implementation of a cost-effective scanner tailored for NDT of medium-size titanium 3D printed biomedical components. The main elements of the scanner; which include a low-energy (80 kVp) portable x-ray unit, and a low-cost lens-coupled detector; can be acquired with a budget less than $ 11000 USD. The low-cost detector system uses a rare-earth phosphor screen, lens-coupled to a dSLR camera (Nikon D800) in a front-lit tilted configuration. This strategy takes advantage of the improved light-sensitivity of modern full-frame CMOS camera sensors and minimizes source-to-detector distance to maximize x-ray flux. The imaging performance of the system is characterized using a comprehensive CT quality-assurance phantom, and two titanium 3D-printed test specimens. Results show that the cost-effective scanner can survey the porosity and cracks in titanium parts with thicknesses of up to 13 mm of solid metal. Quantitatively, the scanner produced geometrically stable reconstructions, with a voxel size of 118 μm, and noise levels under 55 HU. The cost-effective scanner was able to estimate the porosity of a 17 mm diameter titanium 3D-printed cylindrical lattice structure, with a 0.3% relative error. The proposed scanner will facilitate the implementation of titanium LPBF-printed components for biomedical applications by incorporating routine cost-effective NDT as part of the process control and validation steps of medical-device quality-management systems. By reducing the cost of the x-ray detector and shielding, the scan cost will be commensurate with the overall cost of the validated component.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".