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Record W3112727575 · doi:10.18280/ijsse.100502

Buggy Role Cage – Analysis and Design

2020· article· en· W3112727575 on OpenAlexvenueno aff
Anil Babu Seelam, Ayub Khan Aaqif Ahmed, Krishnamurthy H. Sachidananda

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsCageContext (archaeology)Computer sciencePoint (geometry)Structural engineeringAutomotive engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Design and analysis of Buggy roll cage commonly used as a recreational vehicle on off road terrains have been studied. These vehicles are usually modified from their existing design to provide performance and safety. In this research paper, an attempt has been made to design a roll cage for a buggy considering different members of the roll cage. In this context roll cage has been drawn on solid works CAD software and has been analyzed using finite analysis by applying the different boundary conditions. The roll cage has been designed considering the AISI 4130 steel and carbon fiber considering five cases such as front impact, side impact, rear impact, drop test and roll over test to ensure safety of the operator to survive the impact scenario. The main significance of this study is to analyze buggy role cage from safety point of view of operator. From these results it can be concluded that carbon fiber roll cage is also one of the promising alternatives for roll cage design and can be recommended from safety point of view.

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 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)0.000
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.225
Teacher spread0.215 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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