Prediction method of residual stress and deformation in belt grinding of thin-walled ring workpieces
Bibliographic record
Abstract
Thin-walled ring workpieces have been widely used in many fields because of their light weight and compactness. However, due to the characteristics of poor rigidity and low strength, residual stress is inevitably generated during machining processes, which is prone to deformation. In this paper, a residual stress prediction model of thin-walled ring workpieces based on finite element simulation and intelligent algorithm is established, and a deformation prediction method is proposed. Firstly, focusing on the nonlinear pressure distribution in flexible belt grinding, a cut-in depth distribution model is constructed based on Preston equation and Hertz contact theory, and furtherly, the finite element simulation of residual stress is carried out, which can provide sufficient learning data for the intelligent algorithm. Then the residual stress prediction model is established based on support vector regression algorithm, and the hyperparameters of the model are optimized by Bayesian optimization and cross validation. Finally, a deformation prediction model of thin-walled ring workpieces based on finite element method is established. Experimental results show that the average prediction errors of residual stress in X and Y directions are 16.28% and 19.51%, respectively, and the average deformation prediction error is 16.8%, which verifies the accuracy of the prediction models.
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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".