3DCastleBenchy: a process-independent benchmark for additive manufacturing
Bibliographic record
Abstract
Purpose The 3DCastleBenchy has been developed to facilitate wider adoption and use of additive manufacturing benchmarking artefacts which encourage both technical and non-technical users and designers to connect the growing number of technologies available. This tool will help people working with additive manufacturing to gain understanding of the limitations and design rules for each process. Design/methodology/approach Benchmarking is of critical importance for additive manufacturing, allowing for comparisons between technology capability, process optimisation and design guidelines. This work presents the 3DCastleBenchy, a design which balances aesthetic appeal and specific, measurable features which can be used for comparing various additive manufacturing processes. Findings The benchmark design was fabricated with three fundamentally different metal additive processes, laser-directed energy deposition (L-DED), laser powder bed fusion (L-PBF) and metal extrusion (MEX). These resulting parts were then analysed, thereby allowing common defects and limitations of each process to be identified, namely, the overhang limitations of traditional L-DED, the cracking that can occur in L-PBF and the deposition tool path artefacts present in MEX. Originality/value Existing benchmarks typically focus on either tolerance engineering features, or they are purely artistic/demonstrative pieces. The 3DCastleBenchy has been designed to find a balance between these objectives to facilitate communication of design for additive manufacturing concepts.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".