Study of Workpiece Temperature Distribution in the Contact Zone during Robotic Grinding Process Using Finite Element Analysis
Bibliographic record
Abstract
Grinding is traditionally categorized as a finishing process in manufacturing. However, more recently it has been used as a machining process as well. Temperature distribution is crucial for investigation of thermal softening effects in the material removal process and thermal damages on the surface of workpiece. One important aspect to be considered in thermal simulations of robotic grinding is the dynamic behavior of the robot which can have significant influences on the process, especially in those performed with low stiffness robots. Most of the earlier thermal simulations of grinding processes are based on a simplified heat sourc e function representing the grinding wheel effect. In this study, heat generation due to a robotic grinding operation is distributed based on the local chip thickness and friction effect over the corresponding contact zone at the workpiece interface. The calculation of the chip thickness is based on a wear model of the grinding wheel in accordance with an impact-cutting behavior, observed with a high speed camera in our laboratory. Temperature distribution in the workpiece is simulated with a 3D transient thermal finite element (FE) code. Special attention is given to consider the dynamic effect of impact-cutting on the process which is caused by the high rotational speed of the wheel and low stiffness of the robot as a tool holder. Element deletion technique is used to represent the material removed from the workpiece and a well known model of the energy partition ratio is used and modified for the amount of energy entering into the workpiece. Grinding experiments conducted with a flexible robot showed a good agreement among simulation results and measured temperatures.
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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 | 0.001 | 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".