Hybrid AI Systems for Tool Wear Monitoring in Manufacturing: A Systematic Review
Bibliographic record
Abstract
Tool wear is critical to quality, productivity, and sustainability in manufacturing processes. Therefore, accurately monitoring and predicting wear is one of the primary goals of smart manufacturing systems. While AI-based approaches have achieved significant success in this area in recent years, issues such as physical inconsistency, limited generalizability, and low interpretability associated with solely data-driven methods have necessitated the development of hybrid approaches. This study systematically examines the literature published between 2020 and 2025 and comprehensively analyzes hybrid AI systems used in tool wear monitoring. Hybrid systems are categorized into four main groups: physics-based hybrids, knowledge-driven hybrids, transfer learning-based hybrids, and heterogeneous model hybrids. This classification holistically evaluates the synergistic effects and performance gains achieved by combining different methods. The findings demonstrate that the combined use of physical models, expert knowledge, and data-driven learning approaches provides significant advantages in terms of both accuracy and explainability. However, challenges such as data shortage, model complexity, and computational cost remain limitations to widespread industrial use of hybrid systems. The study demonstrates that hybrid AI systems represent a new research direction enabling the development of more reliable, transparent, and efficient solutions in smart manufacturing.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".