MétaCan
Menu
Back to cohort

FEA Modal and Vibration Analysis of the Operator’s Seat in the Context of a Modern Electric Tractor for Improved Comfort and Safety

2025· article· en· W4415816801 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAgriEngineering · 2025
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsnot available
FundersNational University of Science and Technology
KeywordsTractorVibrationContext (archaeology)Finite element methodAccelerationAccelerometerNatural frequencyFrequency domainModal analysis

Abstract

fetched live from OpenAlex

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.262
Teacher spread0.256 · 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