Special Issue on Precision Engineering for Advanced Automation Technology
Bibliographic record
Abstract
This special issue of the International Journal of Automation Technology brings together research advancing the field of precision engineering in the context of next-generation automation systems. Precision engineering has long served as the foundation of industrial innovation, enabling the construction of systems with high precision, efficiency, and reliability under demanding conditions. As automation becomes increasingly intelligent and interconnected, the role of precision is becoming increasingly important, affecting not only the performance but also the reliability, sustainability, and adaptability to complex environments. The contributions reported in this issue cover diverse yet complementary research areas. Specifically, advanced mechanism design, improved uncertainty quantification in early-stage design processes, and development of state-of-the-art measurement techniques based on imaging are covered. Structural optimization approaches aimed at reducing the errors resulting from geometric distortions and misalignments are also explored. By integrating theoretical innovations with practical applications, these studies provide practical insights that engineers and researchers can employ to enhance the capabilities and reliability of automation technologies. Synergies between precision engineering and emerging fields, such as artificial intelligence, robotics, and cyber-physical systems, are expected to lead to transformative advances. We hope that this special issue will encourage continued collaboration across disciplines and foster technologies that not only meet current industry demands, but also anticipate future needs. The editors would like to thank all contributors and reviewers for their efforts to make this special issue a valuable resource for researchers, practitioners, and innovators in the global automation community.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.022 |
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".