A comparison of the distortion of machined parts resulting from residual stresses within workpieces
Bibliographic record
Abstract
The distortion of machined components is a major concern in the manufacture of structural aerospace components. The distribution of machining-induced stresses can affect a component’s ability to withstand severe loading conditions, as well as causing dimensional and geometrical deviations. It can also lead to high rejection rates and quality-related problems during component assembly. It is therefore essential to understand the mechanism that is at the root of parts distortion; this could result both from existing residual stresses in workpieces or induced by the machining process. This paper proposes an experimental approach to determine the influence of existing residual stresses within workpieces on the distortion of parts following machining operations. Two types of raw material were machined; one standard aluminum alloy and one free of residual stress. The stresses were measured before and after the machining process for both material types, using the neutron scattering non-destructive method. The results show that the distribution, signs, and magnitudes of the residual stresses may be at the origin of the deformations measured, which indicates that residual stresses embedded within the raw material are partly responsible for the distortion of the parts following their machining.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".