Bibliographic record
Abstract
The use of advanced machining technologies including conventional and nonconventional processes is growing particularly in aerospace and automotive industries. The special issue aimed to publish high-quality research articles in the field of machining technologies. In this issue of the journal, a selection of some of the technological challenges facing the manufacturing industry is presented and some of the inventive researcher pioneering solutions and findings are introduced. The papers accepted for publication in this special issue cover a range of investigations into experimental and modelling of various machining processes. The first paper discussed the environmental impact of machining Ti-6Al-4V using Minimum Quantity Lubrication (MQL) and was submitted by ( National Research Council of Canada, Montreal and McGill University, Quebec, Canada ). The second paper introduced by Prof. Erween Abd Rahim ( Universiti Tun Hussein Onn Malaysia and Tokyo University of Agriculture and Technology, Japan ) and focused on the development of a three-dimensional finite element model when drilling Ti-6Al-4V. Amin Dadgari ( Newcastle University, UK ) introduced work to investigate the tool path impact on high-speed micro milling. Dr. Nida Naveed ( Sunderland University, UK ) presented the deformation characterisations for the Wire Electro Discharge Machining (WEDM) contour cut surfaces. The last paper focused on the development of a numerical model to simulate the thermal damage when drilling CFRP composites. The model was validated using experimental results collected during the investigation ( National Research Council of Canada, Montreal, McGill University, Quebec, Canada and Airbus Operations S.A.S, France ). We hope that all readers find the issue inspiring, beneficial and has also given a good on-site of the ongoing research and development in this fascinating fast-evolving area of machining technology. We look forward to receiving your contribution to our future journal issues.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.045 |
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".