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Record W3019949353 · doi:10.11575/prism/37717

Integrated Design of Complex Mechanical Products Considering Modeling, Simulation and Optimization Aspects

2020· dissertation· en· W3019949353 on OpenAlexfundno aff
Davood Imaniyan

Bibliographic record

VenueOpen MIND · 2020
Typedissertation
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultidisciplinary design optimizationComputer scienceEngineering optimizationSystems engineeringMechanical engineeringEngineeringManufacturing engineeringOptimization problemAlgorithm

Abstract

The presently developed computer-based design systems are not effective for design of complex mechanical products when multiple tools and methods in different schemes have to be employed at different design stages. In this research, a new integrated framework has been introduced for the design of complex mechanical products considering modeling, simulation/evaluation, and optimization aspects. An integrated system for design of complex mechanical products has also been developed. In this system, first a hybrid scheme is introduced for integrated modeling of complex mechanical products considering conceptual design and detailed design stages. In conceptual design, the generic product considering different design solution candidates is modeled in an AND-OR tree. Specific design candidates modeled by AND trees are created from the generic AND-OR tree through tree-based search. The geometric descriptions in a design candidate are then converted into and associated with the geometric model in a CAD system for detailed design. Second, a hybrid simulation method is developed for evaluating different product aspects with different simulation tools that are integrated through the hybrid modeling scheme. Simulations with geometric descriptions are conducted by analysis functions of the CAD system for detailed design. Simulations with non-geometric descriptions are conducted by the knowledge-based systems for conceptual design. Third, a hybrid optimization method is developed to identify the optimal design of the complex mechanical product. For each design candidate, parameter optimization is conducted to obtain the optimal parameter values. The optimal design solution is identified from all design candidates through configuration optimization. The integrated complex mechanical product design system has been implemented using C# and SOLIDWORKS. Various user interfaces were developed for conducting design activities in modeling, simulation/evaluation and optimization aspects. Communication between the symbolic model in conceptual design and the CAD model in detailed design was achieved through TCP/IP client-server structure and SOLIDWORKS API. A case study has been developed to demonstrate the effectiveness of the newly-introduced design approach.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: fund_new · design weight: 1678.90 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Framework for integrated design, simulation and optimization of mechanical products; engineering design.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The dissertation develops an integrated engineering design system.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Integrated design framework for mechanical products; engineering design methods, not metaresearch.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.085
GPT teacher head0.292
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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