Automated design and implementation of Kalman observer for spindle torque estimation in CNC machining
Bibliographic record
Abstract
Milling is a subtractive manufacturing technique, where the material is continuously removed from a workpiece until the desired part shape is obtained. Heavily used in the automotive and aerospace industries, Computer Numerical Control (CNC) milling machines occupy a large chunk in the manufacturing process. The current research focuses towards machining and machine tool monitoring systems that are more self-sufficient and self-adjusting to adapt the processes to machine tools. Cutting torque delivered by the machine tool spindle is one of the key sensory signals for machining process monitoring. This thesis presents a method that automatically reconstructs cutting torque from motor current commands generated by the servo controller of the machine tool. To estimate the cutting torque from commanded spindle current, the dynamics between torque to current relationship must be modeled and compensated. The thesis first presents an automated identification of spindle dynamics using data-driven system identification methods. The frequency response function (FRF) of the spindle dynamics is measured manually using CNC internal diagnostic tools. The identified FRF is then automatically converted to a state-space model using the Eigensystem Realization Algorithm (ERA). To reduce overfitting, an optimal threshold is applied to the ERA method to limit the identified system order. And to ensure the stability of the identified system, unstable eigenvalues of the system are removed using Schur decomposition. The identified system is then augmented such that the unknown torque input is modeled as a state changed by a random process noise. This augmented system is used to create a Kalman Observer, which compensates the spindle dynamics and estimates the torque from the spindle nominal current. The Kalman Observer is tuned automatically by estimating the noise covariance values using machining simulations. The method was eventually validated on a Quaser UX 600 industrial CNC system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".