Bibliographic record
Abstract
Milling is used to manufacture a wide variety of metal parts, from simple to complex geometries, in small or large volumes. The requirements for these parts usually necessitate that the milling operation is accurate, but also the production rate must be as high as possible. These two requirements, which appear conflicting as first, are met if the milling operation is well planned. While NC programming is still based in many industries on past experience and practical knowledge, research in the areas of cutting mechanics, modelling and simulation are gradually changing the manufacturing practices. This thesis investigates Virtual Milling, which is the integration of milling simulation and CAD/CAM capabilities. Available milling simulation systems can simulate the process for one set of cutting conditions. The objective with Virtual Milling is to not only simulate the milling operation for the whole NC program, but also to integrate features such as feedrate scheduling. A milling simulation for the whole part was developed based on the analytical closed-loop milling model presented by Spence[36]. The input to the simulation is the cutter-workpiece intersections along the tool path. The simulation results include force, torque and power, and deflection along the tool path. The second part of this thesis is the implementation of a Virtual Milling framework. The first step is the selection of cutting conditions which is done during the NC programming. CAD/CAM software do not provide tools to select appropriate cutting conditions, therefore we established the requirements for such a tool using stability lobe theory. An interface was implemented in a commercial CAD/CAM software to demonstrate this. The milling simulation is then used to identify critical locations along the tool path, and it is also used to perform feedrate scheduling. Two offline approaches for feedrate scheduling were implemented, constraint-based feedrate scheduling and off-line adaptive force control, and evaluated to see if their use would lead to improved machining accuracy, better control of cutting forces, and improved machining time. Cutting tests were conducted and these approaches were also compared to an existing online adaptive force 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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".