Metrology test object for dimensional verification in additive manufacturing of metals for biomedical applications
Bibliographic record
Abstract
Additive manufacturing continues to increase in popularity and is being used in applications such as biomaterial ingrowth that requires sub-millimeter dimensional accuracy. The purpose of this study was to design a metrology test object for determining the capabilities of additive manufacturing systems to produce common objects, with a focus on those relevant to medical applications. The test object was designed with a variety of features of varying dimensions, including holes, cylinders, rectangles, gaps, and lattices. The object was built using selective laser melting, and the produced dimensions were compared to the target dimensions. Location of the test objects on the build plate did not affect dimensions. Features with dimensions less than 0.300 mm did not build or were overbuilt to a minimum of 0.300 mm. The mean difference between target and measured dimensions was less than 0.100 mm in all cases. The test object is applicable to multiple systems and materials, tests the effect of location on the build, uses a minimum of material, and can be measured with a variety of efficient metrology tools (including measuring microscopes and micro-CT). Investigators can use this test object to determine the limits of systems and adjust build parameters to achieve maximum accuracy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".