Failure mode investigation and re-design of heavy-duty commercial vehicle bogie bracket
Bibliographic record
Abstract
Heavy-duty commercial vehicles play a significant role in commodity logistics. For each of these vehicles, the suspension is the most essential system to support the load and road shock. Bogie type suspension system is employed to safeguard the vehicle from road shock. The bogie bracket is a juncture between the chassis and the axle in the suspension system. The bogie bracket has been identified as a critical part of the suspension system. In the present study, bogie bracket base design and modelling was performed using computer-aided engineering (CAE). The strength of the bogie was tested to identify weaker sections. Design modifications were performed to improve the strength on identified critical sections through reinforcement techniques. A road load data acquisition (RLDA) test was conducted under different road conditions to validate CAE results. Five different rough-road road surfaces were chosen for RLDA testing. Using strain gauges, strain data were acquired during the test. Corresponding stress values were obtained and maximum stress was found in all driving conditions. For the base design bogie bracket, under RLDA test, crack initiation and crack propagation were identified under vertical loads. A reinforced bogie bracket was designed and found to have a higher strength and longer expected life than that of the base design.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".