Influences of Grit Shape and Cutting Edge on Material Removal Mechanism of a Single Abrasive in Flexible Robotic Grinding
Bibliographic record
Abstract
A flexible robotic grinding system has been used for in situ maintenance of large hydro turbine runners by Hydro-Quebec. Field trials for more than 20 years have proven the reliability and efficiency of the technology for hydropower equipment maintenance and repair. This portable robot named SCOMPI, is developed by IREQ, Research Institute of Hydro-Quebec and can perform high material removal rate grinding on hardly accessible areas of turbine runner blades. Due to the light weight and low rigidity of the robot, traditional position control of conventional grinding is not applicable in this process. Instead a hybrid force/position controller is employed to ensure the accuracy of the predefined material removal rate. Therefore, having a good force model for a specific removal rate is a prerequisite for controlling the grinding task. Understanding the grinding process as the cutting action of several single grits participating in the material removal process provides an insight to predict the needed forces. This paper presents an investigation of the effects of grits shape on cutting forces in single abrasive cutting mechanism during high removal rate grinding by SCOMPI robot. A three-dimensional finite element model is developed to simulate the chip formation process with different grit shapes. Thermal results from our previous study of temperature distribution in the contact zone for this special robotic grinding are imposed to the un-deformed chips. Then, Johnson-Cook plasticity model is employed to investigate effects of hardening and thermal softening of work piece material in cutting forces. It is also found that, rake angle and cutting edges of the grit can have significant effects on the cutting and normal forces.
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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 | 0.000 | 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".