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Record W3004078974 · doi:10.4271/2020-01-0509

Motorcycle Chassis Design Utilizing Multi-Material Topology Optimization

2020· article· en· W3004078974 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 · 2020
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsGeneral Motors (Canada)Queen's University
Fundersnot available
KeywordsTopology optimizationWeightingChassisAutomotive industryConfiguration designComputer scienceOptimal designTopology (electrical circuits)Mathematical optimizationEngineeringMechanical engineeringMathematicsStructural engineeringFinite element method

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Evolving fuel efficiency and emissions standards, along with consumer demand for performance, are strong pressures for light-weighting of performance oriented motorcycles. The field of topology optimization (TO), with the extension of multi-material topology optimization (MMTO) provide manufacturers with advanced structural light-weighting methodology. TO methodology has been adopted in many industries, including automotive where light-weighting assists in meeting efficiency regulations. The development of process specific manufacturing constraints within MMTO is a critical step in increasing adoption within industries dealing with manufacturing cost restrictions. This capability can decrease design complexity, lowering manufacturing costs of optimization solutions.</div><div class="htmlview paragraph">A conventional all-aluminum perimeter style motorcycle chassis is analyzed to develop baseline compliance (total strain energy) metrics. An MMTO design space is created and optimized with steel and aluminum, such that results match the baseline design weight. This formulation demonstrates increased structural efficiency through stiffer structures at equivalent weight. Results are generated with standard MMTO, symmetry, and extrusion constraints to demonstrate utility of these manufacturing constraints. Material ratios are used to enforce lower cost material distribution selections.</div><div class="htmlview paragraph">The usage of MMTO with manufacturing and material ratio constraints has resulted in up to 60.4% reduction in structural compliance of designs, and a multitude of lower cost alternative designs. The usage of symmetry constraints provides effectively identical results to standard MMTO, with a computational time reduction of 29%. Extrusion constraints demonstrate decreased manufacturing difficulty with a computational time reduction of 52% and structural performance penalty of 58.6% from the lowest compliance MMTO result. The enforcement of steel has demonstrated a decrease in structural performance (increase in compliance) and material costs, with varying degrees depending on manufacturing constraints and enforcement limits. The MMTO based designs provide a range of solutions to designers, which can be selected based on the importance of structural efficiency, manufacturing difficulty, and material costs.</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 score0.838
Threshold uncertainty score0.470

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 head0.045
GPT teacher head0.339
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