Bibliographic record
Abstract
The development of unmanned machining systems has been a recent focus of manufacturing research. Computer Numerical Control (CNC) units, which monitor and control many tightly coupled machining tasks, are inseparable parts of the unmanned manufacturing system. This thesis addresses the detailed design and analysis of a modular CNC system for a milling machine. The feed drive control system of the machine tool is thoroughly studied. The mathematical model for the feed drive control system, which consists of a motion controller, power amplifiers and DC-servo motors, is developed and experimentally verified. A method of estimating cutting forces from current drawn by feed drive motors is developed. The viability of the current sensor as a feeding-force sensing component is illustrated and experimentally tested on the milling machine. Successful application of the current sensor to tool breakage detection in milling operation is presented. The performance of the multi-axis contouring system in precision machining is discussed. The contouring errors induced by feed drive positioning systems in CNC machine tools is investigated using the state space model of the CNC system. The influence of the cutting force on the accuracy of machining is shown to be negligible for feed drives driven by ball leadscrews. Various control strategies for the improvement of machining accuracy are tested in the simulation studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".