MétaCan
Menu
Back to cohort
Record W4410852550 · doi:10.1115/1.4068793

Gradient-Based Optimization of Component Layout: Addressing Accessibility and Mounting in Assembly System Design

2025· article· en· W4410852550 on OpenAlex
Daniel Krsikapa, Il Yong Kim

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

VenueJournal of Mechanical Design · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsComponent (thermodynamics)Engineering drawingComputer scienceEngineeringMechanical engineeringSystems engineering

Abstract

fetched live from OpenAlex

Abstract This article proposes novel methods for capturing and modeling critical physical assembly parameters, with a particular focus on mounting and accessibility requirements, for use in 3D assembly design optimization. The effectiveness of the proposed parameters is validated by integrating them into a gradient-based optimization framework and applying it to three increasingly complex test cases. The proposed framework simultaneously addresses essential assembly requirements: maximizing packing density, preventing part overlap, accommodating components on nonplanar mounting surfaces, maintaining proximity relationships, and ensuring accessibility for high-maintenance components. The results show that the method consistently generates densely packed, feasible layouts that satisfy all defined assembly requirements. This work establishes a strong foundation for practical assembly design optimization. Future efforts will focus on improving computational efficiency, scaling to larger and more diverse problems, and incorporating additional engineering considerations to further enhance the framework's industrial and academic utility.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.271
Teacher spread0.232 · 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