A review of explainable artificial intelligence in smart manufacturing
Bibliographic record
Abstract
Artificial Intelligence (AI) technologies have become essential in smart manufacturing, driving predictive capabilities and operational efficiency. However, the opacity of AI decision-making remains a critical barrier, as it limits interpretability and trust in high-stakes manufacturing environments. Explainable AI (XAI) addresses this challenge by making AI models more interpretable and trustworthy. Yet, due to the relative novelty of XAI, there are substantial challenges in implementation, a lack of standardised frameworks, and limited methods for quantitative evaluation. As a result, current applications of XAI in smart manufacturing remain under-developed, non-standardised, and fragmented. This review thus aims to provide a comprehensive exploration of the current landscape of XAI, highlighting recent advancements and critically examining its role in enhancing trust and transparency in smart manufacturing. Given the increasing reliance on AI for decision-making in complex manufacturing systems, a focused review of XAI is crucial for identifying pathways to more transparent and responsible AI-driven solutions. The paper also discusses key implementation challenges and outlines future research directions, with insights into how XAI could shape the future of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".