Analytical Modelling of Elastic-Plastic Interference Fit Joints
Bibliographic record
Abstract
The interference fit is a widely process, used to produce a joint assembly of two parts achieved by friction. It is a popular type of joint in machine elements and it is found in many field applications related to the automotive, aerospace, oil and gas and shipbuilding industries. Consequently, a better understanding of its limit behavior together with an accurate evaluation of the residual stresses generated by this process are very important for the design of mechanical components requiring high optimized performance. This work focuses on the analytical development of the residual stresses when the two assembled parts are circular cylinders and deform elasto-plastically under plane strain condition. The constitutive law that governs their material strain hardening behavior is assumed to follow a general power law which also covers the particular cases of elastic perfectly plastic and bilinear hardening. To validate the developed analytical model (AM), the finite element method (FEM) was used and the results showed good agreement between the two approaches. The obtained results show that the stresses increase when the interference value increases causing maximum stress intensity at the inner surface of the two assembled parts to exceed their material yield stress. After hollow shaft plastic collapse, any increase in interference results in a small increase in residual contact pressure with a large increase of the equivalent stresses localized at the hollow shaft inner surface. The method could be used to determine the maximum value of the interference in order to prevent assembly failure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".