Geometric tolerance and manufacturing assemblability estimation of metal additive manufacturing (AM) processes
Bibliographic record
Abstract
Metal additive manufacturing (AM) has become a predominant process for manufacturing complex metal parts. However, research on controlling the geometric tolerances of the metal AM printed parts and assemblies is scarce. This paper presents a methodology to conduct a geometric tolerance and manufacturing assemblability study of the parts manufactured by metal AM. An assembly benchmark test artifact (ABTA) is designed to include mating features with given assembly conditions based on geometric tolerancing quantifiers. For virtual analysis, prediction phase ABTA samples are generated by using systematic and random field theory deviations. The prediction phase deviations are then calibrated using deviations from a numerical simulation based on thermo-mechanical finite element model of the part. These samples or ‘skin model shapes’ are subjected to geometric tolerance and assemblability study. For experimental validation of the method, geometric tolerance quantification and actual assembly was conducted on laser powder bed fusion (LPBF) fabricated parts. The comparative analysis of the experimental and virtual results validates the new methodology and its ability to provide reliable information regarding assemblability, size dimensions and geometric tolerances. The method can be extended to any AM process for performing a virtual tolerance and manufacturing assemblability study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".