Numerical investigation of the effect of harrow tine’s geometry on fatigue life
Bibliographic record
Abstract
Abstract Harrow tines experience large deflections due to varying soil conditions, leading to fatigue failure through cyclic loads. Selecting the appropriate coil diameter, pitch, and number of coils is crucial for designing harrow tines that can withstand these deflections. The aim of this research is to develop new harrow tine designs that offer improved sustainability compared to conventional harrow tines used in the Canadian prairies. Nine double helical torsion spring harrow tine designs were developed, differing in coil diameters, pitch, and number of turns, while keeping the wire diameter constant. A comparative analysis was conducted, considering fatigue life, failure criteria, and stress distribution patterns assessed through Finite Element Modeling (FEM). Additively manufactured 38% scaled harrow tine prototypes underwent load-bearing tests using identical load sets of 20, 50, 100, and 200 grams. The 2T3D2P, 1T4D2.5P, and 2T4D2.5P models emerged as reliable harrow tine designs with higher fatigue life of 14,115, 14,438, and 27,618 cycles compared to the frequently used conventional harrow tine’s 7533.87 cycles. Coil diameter has a preferential influence on achieving higher fatigue life, overshadowing the effects of pitch and the number of coils. Furthermore, models with larger coil diameters displayed greater flexibility against the defined weight loads, as observed in the load-bearing tests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".