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Record W2982003429 · doi:10.36289/jtmi.v14i2.132

Analisis deformasi pada coupling element dari automatic mechanical coupler: studi kasus LRT Palembang

2019· article· id· W2982003429 on OpenAlexaff
Achmad Syaifudin, Betti Mawar Kalista, Agus Windharto

Bibliographic record

VenueJurnal Teknik Mesin Indonesia · 2019
Typearticle
Languageid
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoupling (piping)IsotropyStructural engineeringvon Mises yield criterionStress (linguistics)Finite element methodComponent (thermodynamics)EngineeringTransient (computer programming)Deformation (meteorology)Materials scienceMechanical engineeringComputer scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Coupler merupakan komponen yang menyambungkan gerbong kereta. Kekuatan coupler terhadap beban eksepsional dan ketahanannya terhadap beban kerja normal menjadi parameter keamanan yang sangat penting dalam operasinya. Kajian ini melakukan analisis deformasi secara numerik terhadap coupling element dari automatic mechanical coupler yang digunakan pada tipe kereta ringan (Light Rail Transit, LRT) berdasarkan beban aktual yang diterima. Bagian coupling element yang diteliti adalah coupling link dan hooked plate. Model solid 3D dibuat tanpa penyederhanaan untuk hasil simulasi yang lebih mendekati fenomena aktual. Material isotropik yang digunakan adalah baja ASTM A633 Grade E. Simulasi statis dan transien dilakukan untuk mendapatkan faktor keamanan statik dan dinamik. Simulasi fatik dari analisis pembebanan transien dengan tiga mode pembebanan dilaksanakan untuk memperkirakan umur kerja coupling element. Hasil simulasi menunjukkan bahwa coupling element yang diobservasi aman terhadap beban eksepsional dan normal yang diberikan. Selain itu, simulasi mengindikasikan bahwa komponen yang kritis berdasarkan tegangan von Mises maksimum adalah coupling link dan komponen yang kritis berdasarkan siklus umur adalah hooked plate.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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
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

Citations4
Published2019
Admission routes1
Has abstractyes

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