Influence of surface roughness in turning process — an analysis using artificial neural network
Bibliographic record
Abstract
This paper presents methodology to identify the surface roughness value in CNC machining process using a soft computing approach. The aim of this paper is to achieve a roughness accuracy value above 95% and reduce the error rate to below 5% by using an artificial neural network. An artificial neural network method was selected to improve the time of inspection. Fourier transformation method will be used to extract the turning workpiece image, which is the squared value of the major frequency and principal component magnitude. Primary machining parameters such as feed rate, depth of cut, speed, frequency range, gray scale value, and conventional measurement value feed are used as the training input in the artificial neural network. Based on the training sample, the artificial neural network generates the vision measurement value for the testing samples that is compared to the stylus probe measurement value to predict the error rate and accuracy. The novelty of this work is to create an effective methodology using artificial neural network techniques to detect surface roughness errors of materials used in manufacturing industries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".