Compression‐Induced Fracture of Maize Kernels: Effects of Moisture Content and Strain Rate on Mechanical Behavior
Bibliographic record
Abstract
ABSTRACT During production processes, maize kernels are prone to mechanical damage from stresses such as collision and compression, leading to reduced storage stability, increased mold contamination risks, diminished processing performance, and consequent economic losses. Investigating the mechanical properties and fracture mechanisms of maize kernels is critical for reducing breakage and losses while safeguarding food security. This study systematically analyzed the effects of moisture content (9.75%, 13.07%, 16.66%, 20.67%, 25.21%) and compression speed (0.5, 2, 5, 50 mm/min) on anisotropic mechanical behavior through triaxial compression tests using a universal testing machine, integrating displacement–load curves, Dynamic Increase Factor (DIF), and Crash Force Efficiency (CFE). Results indicate that elevated moisture content significantly reduces mechanical strength, while increased compression speed partially counteracts moisture‐induced softening via strain rate hardening effects. The minor axis exhibited the highest elastic modulus (891.78–1041.48 MPa) due to structural compactness, yet its DIF values (1.24–1.71) displayed pronounced sensitivity to moisture variations, reflecting greater operational condition dependency. The intermediate axis demonstrated superior energy absorption capacity, with CFE values (49.6%–64.2%) consistently exceeding those of the major axis (39.51%–53.79%). This research elucidates moisture‐rate interaction patterns governing triaxial mechanical responses in maize kernels, providing theoretical foundations for optimizing the design and operational parameters of corn processing equipment.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".