Classification of Dimensional Deviation in Additive Manufacturing LPBF Process for AlSi10Mg Alloy According to ISO 286 and ANSI B4.2
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) processes are gaining popularity and are currently used in many research activities including the biomedical applications, the automotive industries and the aerospace. Laser powder bed fusion (LPBF) is an important AM process. Metallic LPBF process is experiencing significant growth, but one of the difficulties facing this growth is limited knowledge of its dimensional and geometrical performances, in addition to the inability to predict it. In this paper, we present the dimensional deviations of some LPBF-manufactured parts selected for this investigation. a uniform method was developed regarding relevant test specimens to examine dimensional deviations in order to derive dimensional tolerance values. The manufactured test specimens were measured to examine the process dimensional deviations behavior. These parts were manufactured from AlSi10Mg powder using an EOSINT M280 printer. The results show possible dimensional tolerance values that were classified from IT1 to IT11 according to the international standard ISO 286.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".