A novel transformer-based neural network model for tool wear estimation
Bibliographic record
Abstract
Abstract This paper proposes a novel Transformer-based neural network model for accurate tool wear estimation to improve production quality and efficiency in intelligent manufacturing. The proposed method can realize indirect measurement of tool wear. Initially, the raw multi-sensor signals are processed into three kinds of temporal feature data. Next, three identical submodels are utilized to deal with the above feature data, respectively. Finally, the outputs of these three submodels are concatenated together as the input of a multi-layer fully connected network for the final estimation of tool wear. Concretely, the submodels used in this work are based on the Transformer model and self-attention mechanism to capture long-term dependency. This is the first attempt to adopt Transformer and self-attention for tool wear estimation. Besides, some improvements are made in this work. For example, a long short-term memory network is employed to enhance the ability of capturing position information. In addition, a submodel framework is applied to process the temporal feature data in parallel, which helps improve the model performance. The proposed method is demonstrated through a real-world milling dataset, including more than 900 experiments. Also, the superiority of the proposed method is verified by the comparison with other advanced methods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".