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Record W7162257949 · doi:10.1299/jsmetld.2025.34.tl1-1

A Multi-objective Optimization Approach for Automotive Disc Brakes: Integrating Castability, Strength, and NVH

2025· article· en· W7162257949 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of the Transportation and Logistics Conference · 2025
Typearticle
Languageen
FieldEngineering
TopicBrake Systems and Friction Analysis
Canadian institutionsAnsys (Canada)
Fundersnot available
KeywordsNoise, vibration, and harshnessAutomotive industryNoise (video)Minification

Abstract

fetched live from OpenAlex

Brake squeal in automotive disc brakes remains a significant quality challenge. This issue is further complicated by design requirements for weight reduction, strength, and manufacturability, which often are conflicting; addressing these factors individually in the current process has led to development rework. In this study, a multi-objective optimization method that accounts for brake squeal in the early design stage was established, targeting objectives related to structural strength and stiffness, noise and vibration and harshness (NVH), and castability. Although castability evaluation typically relies on large-scale fluid dynamic analyses, a heat transfer analysis was introduced from an analogical perspective to enhance efficiency. Furthermore, since brake squeal is a bifurcation phenomenon exhibiting discrete characteristics, an evolutionary algorithm (an AI-based optimal solution search method) was employed.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.232
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it