Investigating Peak Power and Energy Measurements to Identifying Process Features in CNC Endmilling
Bibliographic record
Abstract
Energy costs associated with manufacturing processes represent an expense currently beyond the control of manufacturers. As a result, many industries have begun to consider how to reduce energy consumption demands while still maintaining or increasing process efficiencies. All manufacturing processes have an associated energy cost. For machined parts, individual processes used to machine the overall part have measureable energy costs associated with them. Properly linking peak power and energy consumption with machining processes requires characterizing the machine tool and machining process with respect to measured power. By doing this, process specific features can be linked to elements of the resulting peak power of the machining process. Building off previous works in characterizing power consumption with respect to material removal rates (MRR), the current paper examines peak power and energy consumption during the endmilling of two standard test parts. Using direct measurement techniques and a predefined geometry of two test parts, peak power is measured for a CNC machine tool and the machine spindle. The resulting power signals are shown to be sensitive enough to be linked to process changes and process features that occur during the machining process. Power and energy data is linked to the metal cutting process and linked to the identification of process changes, with specific changes in the power measurements linked to cutter location and process features.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".