Artificial intelligence applied in adaptive manufacturing process monitoring: a state-of-the-art in the era of automation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Manufacturing productivity performance continues to be a significant challenge in industrial environments due to frequent unforeseen changes in process conditions. Unanticipated changes generate disturbances, leading to defects in finished products and deviation from established specifications. Therefore, it is important to optimize process parameters dynamically. This article aims to describe the current state of dynamic optimization of manufacturing process parameters in the context of Artificial Intelligence. Research in the Compendex database led to the identification of 106 records, from which 16 were retained and analyzed. The industrial contexts addressed in this field of research, the types of data used and their pre-processing, as well as the methods employed to detect anomalies and their causes regarding input parameters’ impact on quality and productivity were reviewed. Our results reveal a lack of attention to large-scale manufacturing industries and a scarcity of categorical variables in dynamic optimization of manufacturing process parameters. This presents a promising opportunity for future work in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it