Adaptive benchmarking design for additive manufacturing processes
Bibliographic record
Abstract
Abstract Standards enabling the objective tolerancing and evaluation of dimensional and geometrical performances of additive manufacturing (AM) printers are still missing. The design, printing and measurements of geometrical benchmark test artefacts (GBTA) is the current solution proposed in literature. However, the current GBTA with fixed dimensions cannot cover most of the available printing area of printers with large building platform dimensions. This article proposes to solve this problem by developing an adaptive GBTA design whose main dimensions can be adapted to any common 3D printer. Moreover, an innovative design is implemented to decrease the risk of warping. The adaptive GBTA will then be used to characterise the performances of two different architecture material extrusion printers (Ultimaker 2+ and Pollen AM Series MC). Dimensional and geometrical accuracy, as well as top surface topography, were evaluated. The Ultimaker printer could reproduce features with maximum deviations below the tolerance interval (IT) 13 of the ISO 286-1, while the Pollen machine achieved a higher IT of 15 or 16. The highest geometrical deviations were observed for the coaxiality of cylinders oriented along the build direction (Ultimaker: 0.250 mm and Pollen: 0.497 mm). Top surface topography exhibited higher Ra values for Pollen (13.7 µm) than for Ultimaker 2+ (4.9 µm). The performances of the Pollen printer were lower than the Ultimaker machine in terms of surface topography, dimensional and geometrical accuracy. The proposed adaptive GBTA design covers most of the printing areas exhibited by Pollen and Ultimaker printers and offers flexibility to test other printers even with larger or smaller dimensions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".