Surface Roughness Classification Using Attention-Guided Convolutional Neural Networks on Real-Time Machining Sensor Data
Bibliographic record
Abstract
Surface roughness classification is the most important measure in the pursuit of machining quality, but conventional offline measurements are not conducive to real-time monitoring. To address this issue, the paper presents an Attention-Guided Convolutional Neural Network (AG-CNN) for classifying levels of surface roughness from multivariate force signals (Fx, Fy, Fz). The model combines 1D convolutional layers with a temporal attention mechanism to concentrate on signal regions. The system is deployed with Python and TensorFlow, which allows effective preprocessing, training, and inference of the Models. To make use of the publicly released CNC Turning dataset, where force signals are normalized, segmented, and annotated as smooth, moderate, and rough classes in signals. The introduced AG-CNN achieves an accuracy of 85.3%, representing a 5.1% improvement over the baseline CNN. The findings verify that incorporating attention improves classification performance and provides a stable framework for real-time monitoring of surface quality in intelligent manufacturing systems.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".