Influence of Material Properties on the Durability of Automotive Hydraulic Brake Discs: A Finite Element Analysis Approach
Bibliographic record
Abstract
The brake system in automobiles is critical for ensuring both safety and operational efficiency, enabling the vehicle to reduce speed or come to a complete stop when required.Among the components of the brake system, the brake disc plays a pivotal role, undergoing regular operation and being subjected to considerable mechanical stresses, thermal fluctuations, and wear.As such, ensuring the durability of the brake disc is paramount for the overall performance and safety of the vehicle.This study investigates the influence of different materials on the durability of automotive hydraulic brake discs by evaluating stress, deformation, heat generation, and safety factors.A 3D model of the brake disc was developed using SolidWorks, and the finite element method (FEM) was employed to analyze the system's response under operational conditions using ANSYS.The results indicate that the gray cast iron material demonstrates the most favorable performance, exhibiting a deformation of 0.03987 mm, a maximum stress of 57.457 MPa, a heat generation of 67.363℃, and a safety factor of 3.1328.These findings suggest that gray cast iron is a viable material for manufacturing brake discs, offering a balance of mechanical strength, thermal management, and durability.This study provides a foundational understanding of material properties for the design and mass production of automotive brake discs, contributing to the development of safer and more efficient braking systems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".