An experimental evaluation fatigue life of unnotched rail specimen under random vibration fatigue analysis
Bibliographic record
Abstract
The aim of this paper is to propose a prediction of the vibration fatigue life in frequency domain utilising newly methods that considers the vibration-load sequences effect under random vibration loading. The design of experiment employed a modal analysis to examine the vibration responses signals of R260 steel in loading ranges 300–800 MPa, however three specimens selected with different size capacity of stresses of 350 MPa, 687 MPa and 750 MPa. The vibration signals are transformed from time domain to frequency domain using power spectral density through pwelch tool. To address the stress amplitude probability distribution, each vibration responses signal uses the rain-flow counting method to analysis every minimum and maximum peaks. The fatigue life of random vibration loading was investigated through two frequency domain approaches namely Dirlik and Rayleigh distribution. The results of fatigue life of Dirlik indicate that through (g2/psd) such as 0.031, 0.16 and 0.65 it exhibits the lower fatigue life prediction with 695,000, 39,000 and 450 cycles respectively, compared to Rayleigh with 750,000, 41,000 and 750 cycles for three specimens with different size capacity loading. This work gives high accuracy and a good practical for predicting model to contribute in vibration fatigue topic for academic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".