Prediction of Surface Roughness for CNC Turning of EN8 Steel Bar Using Artificial Neural Network Model
Bibliographic record
Abstract
Manufacturing organizations are under tremendous pressure to produce quality products at reduced manufacturing cost with shorter time-to-market.A good combination of process parameters for any manufacturing process may ensure production of quality parts at reduced time and cost.Aim of the present study is to develop an artificial neural network (ANN) model for CNC turning operation to predict surface roughness of EN8 bar for a specific set of parameters.In this study, effect of CNC turning parameters, namely spindle speed, depth of cut and feed rate, each with three levels, on surface roughness of EN8 steel bar has been studied experimentally.Total 27 experiments have been conducted as per full factorial design of experiment approach and surface roughness of the turned pieces is evaluated using a Mitutoyo make roughness tester.One set of the roughness values has been used to train the ANN model and another set is used to test the predictability of the model in MATLAB 8.0 software.The average error between the experimental and predicted values is found to be 7.85 %.
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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 | 0.001 |
| 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".