S0410 Tools for adaptive and intelligent control of discrete manufacturing processes TANDEM - Final Project Report
Bibliographic record
Abstract
The TANDEM project aimed at establishing a data-driven and artificial intelligence-based monitoring and controlling platform and tools that support robotic discrete manufacturing processes, cells, or production systems. The control systems and tools enable an agile, flexible, and quickly reconfigurable manufacturing unit with short ramp-up times and better productivity and quality. Furthermore, extensive manufacturing data collection, warehousing, and analysis enable full traceability and enhance digital quality assessment of the product. The development of the novel tools took place in three industrial demonstrator applications that also cover different scales of manufacturing from individual process equipment to manufacturing cells, to production systems.The overall TANDEM objective was to achieve a high level of automation and remove time and resource consuming manual manufacturing tasks. This requires smart offline programming and process planning and robust and flexible control systems based on AI/ML that can learn from process data and compensate for the deviations that occur due to inaccuracies in tool manipulation, fixturing, varying work piece geometries, heat distortions etc. This is achieved by development of a novel control strategies for the addressed processes to give high consistent quality of the products by applying technologies, such as AI/ML, to achieve intelligent process control.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".