MétaCan
Menu
Back to cohort
Record W3172747666 · doi:10.3934/mbe.2021224

Numerical assessment of directional energy performance for 3D printed midsole structures

2021· article· en· W3172747666 on OpenAlexaff
Ankhy Sultana, Tsz-Ho Kwok, Hoi Dick Ng

Bibliographic record

VenueMathematical Biosciences & Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnergy (signal processing)Measure (data warehouse)Computer scienceEnergy transferTopology (electrical circuits)Efficient energy useEnergy transformationMechanical engineeringSimulationEngineeringPhysicsElectrical engineeringEngineering physics

Abstract

fetched live from OpenAlex

Energy can be represented in the form of deformation obtained by the applied force. Energy transfer is defined in physics as the energy is moved from one place to another. To make the energy transfer functional, energy should be moved into the right direction. If it is possible to make a better use of the energy in the right direction, the energy efficiency of the structure can be enhanced. This idea leads to the concept of directional energy transfer (DET), which refers to transferring energy from one direction to a specific direction. With the recent development of additive manufacturing and topology optimization, complex structures can be applied to various applications to enhance performances, like a wheel and shoe midsole. While many works are related to structural strength, there is limited research in optimization for energy performance. In this study, a theoretical approach is proposed to measure the directional energy performance of a structure, which can be used to measure the net energy in an intended direction. The purpose is to understand the energy behavior of a structure and to measure if a structure is able to increase energy in the desired direction.

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)0.000
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 score0.593
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Biosciences & EngineeringSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207