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Record W3121494166 · doi:10.4271/2021-01-0361

Automotive Hood Panel Design Utilizing Anisotropic Multi-Material Topology Optimization

2021· article· en· W3121494166 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

VenueSAE International Journal of Advances and Current Practices in Mobility · 2021
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsGeneral Motors (Canada)Queen's University
Fundersnot available
KeywordsAutomotive industryTopology optimizationStiffnessAerospaceComputer scienceOffset (computer science)Optimal designMechanical engineeringStructural engineeringEngineeringFinite element methodAerospace engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Topology optimization (TO) represents an invaluable instrument for the structural design of components, with extensive use in numerous industries including automotive and aerospace. TO allows designers to generate lightweight, non-intuitive solutions that often improve overall system performance. Utilization of multiple materials within TO expands its range of applications, granting additional freedom and structural performance to designers. Often, use of multiple materials in TO results in material placement that may not have been previously identified as optimal, providing designers with the ability to produce novel high performance systems. As numerous modern engineering materials possess anisotropic properties, a logical extension of multi-material TO is to include provisions for anisotropic materials. Herein lies the focus of this work.</div><div class="htmlview paragraph">A TO algorithm capable of considering anisotropic material properties is used to investigate a case study on the design of an automotive hood panel. A baseline aluminum hood panel is used to generate stiffness targets for optimization, followed by the generation of a design space model to allow the algorithm to determine optimal material placement. Optimization is undertaken with two types of AS4 continuous carbon fiber reinforced epoxy, each in two orientations. Optimal hood panel solutions that maintain stiffness levels of the conventional baseline are achieved. The mass of the design space is minimized, and constrained through the baseline displacement values. The effect of hood panel thickness and offset distance between panel layers is also investigated.</div><div class="htmlview paragraph">The optimal topologies indicated an overall mass savings of up to 44.5% in relation to the baseline, while maintaining hood panel stiffness. Comparative mass savings decreased as hood panel thickness increased and offset distance decreased. The allocation of stiffer materials was observed near locations of applied loads and constraints, with highly anisotropic materials placed along hood panel extremities. The practicality of anisotropic multi-material TO in lightweight design was thus demonstrated.</div></div>

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)0.001
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 score0.743
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.337
Teacher spread0.294 · 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