COMPARISON OF THE PERFORMANCE OF INDIRECT EVALUATION OF FLANK WEAR FOR TURNING INCONEL-718 USING THE PROCESSED IMAGE AND ACOUSTIC WAVES
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates the tool wear for turning Inconel-718 using the titanium nitride carbide tool inserts. This research work aims to compare the performance of processed images and acoustic waves used as an indirect technique for evaluating tool wear. The work targets to capture the tool wear and tool image after turning and acoustic wave during turning for each experimental run. The pixel area of the processed picture, the root-mean-square (RMS) of the acoustic wave, and the microscope tool wear of the tool maker were taken into consideration as output parameters for the change of operational parameters including feed, speed, and depth of cut. The performance of wear, pixel area, and RMS was compared using the Box Behnken method. Further, the correlation between the performance of tool wear, image processed pixel area, and RMS for the variation in input variable was obtained from interaction and main effects plots. The results demonstrated that at lower speeds (280[Formula: see text]rpm), lower feed rates (0.04[Formula: see text]mm/rev), and medium depth of cut (0.2[Formula: see text]mm), there was less wear, pixel area, and RMS. Wear, pixel area, and RMS have all decreased as a result of the tool and workpiece having less surface friction due to the reduced speed, feed, and medium depth of cut. From the analysis, it was also clear that the indirect evaluation of the wear can be successfully carried out using digital image pixel area and acoustic wave RMS for turning Inconel-718 using a titanium nitride-coated carbide tool.
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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 | 0.001 | 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 it