Robotic Cutting of Fruits and Vegetables: Modeling the Effects of Deformation, Fracture Toughness, Knife Edge Geometry, and Motion
Bibliographic record
Abstract
There is a huge potential for automation of cutting fruits and vegetables in the kitchen and food industry as this can not only save time and labor on meal preparation and food packaging but also improve workspace safety. Foods may undergo large deformations, and the knife can experience different forces, which together make it difficult to carry out cutting as intended. With an accurate model, the contact force between the knife and the cutting board can be extracted from the sensor data for control to realize a smooth knife movement. In this article, we apply the finite element method (FEM) to estimate deformation and the cutting force, and linear elastic fracture mechanics to predict fracture. 3-D FEM is computationally prohibitive, since numerous tiny elements must be repeatedly regenerated around the knife edge as it moves further into a food item. To address this issue, we perform 2-D FEM modeling of parallel slices of the object and use interpolation to iteratively update forces and fracture, the latter of which is predicted when the energy release rate exceeds the material's fracture toughness. A strain-based analysis quantifies the reduction in the cutting force when the knife is in a slicing motion. Modeling is completed over the effects of the knife's edge geometry and cutting path (with translation and rotation). The scheme is then incorporated into an existing control strategy Mu et al. 2023 to perform the knife skill of rock chop on soft objects. Experiments conducted over various types of natural food have demonstrated the accuracy of our model and its potential for real-time cutting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".