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Record W7124327785 · doi:10.23977/jemm.2025.100209

Structural Design and Performance Analysis of Lightweight Manipulator Based on Multi-objective Optimization

2025· article· W7124327785 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2025
Typearticle
Language
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Multi-objective optimizationParametric statisticsTopology optimizationRobotEngineering optimizationPareto principleEngineering design processBasis (linear algebra)

Abstract

fetched live from OpenAlex

To address the multi-performance conflict in lightweight robotic arm design, this study establishes a systematic design framework integrating multi-objective optimization. By combining topology optimization with parametric modeling to develop lightweight structural solutions, and applying advanced optimization algorithms to resolve multi-objective coordination issues involving mass, stiffness, and dynamic characteristics, the framework generates a Pareto solution set that characterizes performance trade-offs. A comprehensive performance evaluation system is further developed to assess the synergy between static-dynamic characteristics and energy efficiency precision, validating the optimization outcomes. The research demonstrates that this framework effectively guides designers in achieving optimal balance between structural lightweighting and overall performance under multiple constraints, providing a methodological basis for high-performance robotic arm development that combines theoretical rigor with engineering applicability.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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