Geometric tolerance and manufacturing assemblability estimation of metal additive manufacturing (AM) processes
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it